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100 Inspiring Statistics Research Topics

Definition and importance of statistics.

The definition of statistics is always different, depending on your subject and methodology. In simple terms, it is a defined study, analysis, and manipulation of data that must be reviewed. The complex part of statistical analysis is drawing conclusions or coming up with reports. Since it all comes down to data interpretation, one must think about choosing good statistics research topics. Start by addressing various scientific, industrial, or social problems. It will make it easier to narrow things down and find the most efficient solution, be it a manual statistical interpretation or software automation. If things do not work for you, remember that you can pay for research paper and receive additional help with calculation or methodology choice. It will also help you to quote every statistical bit of data correctly if it has been taken from an outside source! 

Statistics Research Topics

How To Write Statistics Research Topics?

The trick here is to know what methodology will be used to collect and interpret statistical data. Even if you have not chosen your statistics project topic, think about it before going any further. It will help you learn about what kind of data will be researched as the sample will be picked correctly. Your basic outline for choosing the right topic should be this way:

  • Introduction of a problem or a scenario. 
  • Methodology choice and explanation. 
  • Statistical research itself in the body part. 
  • Samples variables and deviations. 
  • Statistical interpretation as your conclusion part. 

Always provide sources for statistical data that has been referred to if it is not your first-hand obtained data! 

100 Research Topics For Statistics 

- good statistics research topics .

It must be noted that statistics are required by numerous disciplines these days, which is why choosing something good for your research can deal with anything. Starting with social media analysis to an estimation of students that have passed the exam successfully, all of it can relate to good stat research topics. 

  • The pros and cons of regression analysis. 
  • How accurate is the use of AI-based tools in statistical analysis?
  • The online news reports and the fluctuations: statistical reports. 
  • How can statistical discrepancy be fixed with the help of estimation methods? 
  • National income and the regulation of cryptocurrencies. 
  • Covid-19 vaccination in the United States and effectiveness of treatments statistics. 
  • Mathematical prediction models vs observation strategies. 
  • Bias in quantitative data analysis in Sociology studies. 
  • Descriptive statistics vs inferential analysis methods.
  • Artificial sampling and the role of estimation in modeled statistics. 

- Statistics Topics For Research Project 

When you have to choose a specific topic that will reflect statistical methods, start by narrowing things down or determining what kind of analysis will be used. You can think about the role that statistical analysis plays in a certain field. See some examples below: 

  • Social media and sample size determination methods. 
  • SAS and coefficient of variation mistakes: manual vs automatic calculation. 
  • Interpretation of statistical results when using SPSS reporting system.  
  • The role of standard deviation when using Z-test analysis for social subjects. 
  • The reasons for using ANOVA testing when dealing with online surveys. 
  • Commercial hypothesis approach to online trading: why statistical analysis won't be accurate. 
  • How can diversity be explained with the help of statistical analysis?  
  • The link between playing video games and the violent outbreaks among college students: statistics. 
  • College loans: the reasons why the numbers increase and the related controversy. 
  • Analysis of the Markov Chain for determination of statistical limitations. 

- Ph.D. Research Topics In Statistics 

As a rule, dealing with Ph.D. is supposed to be more challenging, yet statistical methods still remain the same. It is the subject and the data sample set that go through the changes. For example, you can choose electrical engineering for your statistical method to match the Ph.D. academic level. 

  • The use of statistical analysis in Quantum Physics: pros and cons when accuracy is essential. 
  • How can numerical calculations help with theoretical polymers. 
  • Data Assimilation when dealing with Big Data processes: statistical variables. 
  • The role of probability when one must apply Stochastic Analysis methods. 
  • Applicable Analysis and the benefits of statistical surveys for social distancing. 
  • Improvement of prediction methods in aeronautics. 
  • Cryptocurrencies and the statistical methods for SWIFT transactions. 
  • Why acceleration in the banking sector can be more harmful than useful today? 
  • Cell response and genetics: manual analysis and temporal factor. 
  • Business intelligence analysis methods: human analysis vs automatic computation. 

- Easy Statistics Research Topics 

If you want to find an easy statistics research area, think about the real-life application of statistics. It will help you choose easier research topics for statistics. Just make sure to provide explanations of how statistical research helps, talk about why it happens, and how exactly. 

  • The role of statistics in Data Mining processes. 
  • The real-life use of entropy estimation in engineering fields. 
  • Statistical analysis in the field of speech recognition. 
  • Online grammar checking and the use of empirical entropy methods. 
  • Ranking statistical approach when estimating the accuracy of college exams. 
  • Estimation of probiotics: how much time is necessary for an accurate statistical sample? 
  • How will the population of the United States increase in twenty years? 
  • What statistical methods are most useful for active sports? 
  • The legislation and the statistical reports dealing with controversial subjects. 
  • Transparency of statistical methods and the U.S. Census Bureau reporting system. 

- Survey Methods Statistics Research Topics 

Not a single statistical method may go without some sort of survey method. It is one of the reasons why we have included various statistics research paper topics that are based on surveys and their practical examples. 

  • The pros and cons of online surveys for business purposes. 
  • Data collecting and the use of the predefined groups. 
  • Analysis of strengths of multiple-question survey method: Geographical dependence and statistical survey methods
  • Sampling accuracy and the link to wording in survey questions. 
  • How can statistical studies become more cost-effective by turning to surveys? 
  • Drawing survey conclusions and imputation techniques. 
  • The challenges of super population models in healthcare researching. 
  • What has the Covid-19 pandemic revealed about inaccurate surveys in 2021? 
  • Differential calculus versus spatial statistical research methods. 
  • Inner calculation formulas are most commonly used in online surveys. 

- Business Statistics Research Topics 

When you need something statistical for your business, think about estimation, prognosis, and analysis. In the majority of cases, you shall deal with economics and finances to provide the pros and cons of certain methodologies. See some statistical research topic examples that relate to business matters: 

  • Economic data analysis when dealing with probabilities. 
  • Data distribution when working with descriptive samples: violations, bias, and privacy matters. 
  • Inferential statistics for small business owners: things one must know. 
  • The peculiarities of business data sampling: pros and cons of software solutions. 
  • Linear regression analysis: how can two different business projects be approached at once? 
  • Index numbers, random probability, and accuracy in economic data relations. 
  • Programming statistics: the benefits of dataset approach to statistics. 
  • Commercial statistics: how should the information be prepared for the best accuracy? 
  • Suggestive evidence vs real evidence among business corporations. 
  • AI-based statistical report analysis: financial calculation vs human estimation methods. 

- Applied Statistics Research Topics 

If your college professor asked you to deal with applied statistics for your next assignment, have no worries because applied statistics are related to practice. For example, you can provide a certain case or turn to an actual event where statistical practice can or will be used. Once you choose a case study, narrow things down and see our examples: 

  • The challenges of statistical analysis and unstructured data. 
  • The pros and cons of text mining methods and educational statistics. 
  • Scientific approach to analysis vs basic social media analysis methods. 
  • Statistical software: what kind of data should not pass through classic solutions? 
  • The field of healthcare and processing of sensitive statistical information. 
  • Energy sources and statistical estimation of the green energy benefits. 
  • Changes in politics and turbulence in economic estimation. 
  • Globalization and statistical information coming from more than one source. 
  • Composition of a manual statistical report for unstable political environments. 
  • The percentage of aggression in active sports: behavioral statistics. 

- Sports Statistics Research Topics 

Regardless of whether you are making bets on sports or want to find out who might win the game by turning to scientific methods, sports statistics is what you will need. See some good statistics research topics about sports to get inspired: 

  • Why are Pareto charts preferred more compared to bubble charts for baseball series? 
  • Data analysis in sporting events: a comparison of baseball and basketball statistics.
  • Recording live data sets: how can median accuracy be achieved? 
  • The role of free agencies for ranking purposes: bias in sports statistics.
  • The peculiarities of NFL statistician's work and the reporting privacy. 
  • Why does the average distance gained per running session matter in soccer? 
  • Past games analysis information vs future estimates. 
  • The role of the news organization and the live changes to statistical estimation. 
  • Inter-sportive data and the commonalities encountered by statisticians in NBA and NFL. 
  • Knowledge of strengths and weaknesses of teams and their importance for accurate statistics in sports. 

- Possible Research Topics For Statistics 

Here are the possible subjects where statistics can be researched. If you are not good with formulas and mathematical calculations, these inspiring statistical research topics will help you: 

  • Cyberbullying and common patterns used for online attacks: collecting stats. 
  • The role of statistics during political campaigns and elections. 
  • The success ratio of male vs female employees in Amazon Inc. 
  • The role of sociological estimation for the establishment of statistical data sets. 
  • How can data mining practices help establish more accurate statistics? 
  • The role of urgent calculation for statistical reports in the military. 
  • Estimation of descriptive statistics during Covid-19 times. 
  • Western and Southern African statistical reporting agencies: case study comparison. 
  • Statistical bias in politics: why transparency cannot be achieved. 
  •  The use of statistical estimation in mechanical engineering. 

- Psychology Research Topics for Statistics 

Psychology, Sociology, Healthcare, and Education are among those topics where statistical analysis is essential. Since psychology encompasses philosophy and the constant variables, depending on the case, these statistical psychology topics will be helpful. 

  • PTSD and understanding of descriptive trends in Psychology. 
  • Statistical testing and probability's importance: the role of the researchers. 
  • Acceptable significance and probability levels in clinical Psychology. 
  • The use of hypothesis and the role of P 0.05 for better comprehension. 
  • What statistical data is usually rejected in Psychology? 
  • The use of elementary statistical principles and reasoning in psychological analysis. 
  • The role of correlation when there are several psychological concepts at play. 
  • Statistical reporting based on actual case study learning and modeling. 
  • Naturalistic observation as a scientific sample in Psychology. 
  • How should descriptive statistics capture behavioral data sets? 

Fitting Your Statistical Research Correctly 

One of the most common challenges with statistics is knowing where to fit related data without making it look awkward. If you are in such a situation, our friendly experts are here to provide assistance. As they write research papers for money , they provide you with custom-tailored ideas and will help you avoid plagiarism as you refer to existing case studies. If you want to end up with a great statistical research project, asking for additional guidance is only natural! Take your time to research our list of inspiring statistical topics for research paper and get help when necessary! 

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500+ Statistics Research Topics

Statistics Research Topics

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data . It is a fundamental tool used in various fields such as business, social sciences, engineering, healthcare, and many more. As a research topic , statistics can be a fascinating subject to explore, as it allows researchers to investigate patterns, trends, and relationships within data. With the help of statistical methods, researchers can make informed decisions and draw valid conclusions based on empirical evidence. In this post, we will explore some interesting statistics research topics that can be pursued by researchers to further expand our understanding of this field.

Statistics Research Topics

Statistics Research Topics are as follows:

  • Analysis of the effectiveness of different marketing strategies on consumer behavior.
  • An investigation into the relationship between economic growth and environmental sustainability.
  • A study of the effects of social media on mental health and well-being.
  • A comparative analysis of the educational outcomes of public and private schools.
  • The impact of climate change on agriculture and food security.
  • A survey of the prevalence and causes of workplace stress in different industries.
  • A statistical analysis of crime rates in urban and rural areas.
  • An evaluation of the effectiveness of alternative medicine treatments.
  • A study of the relationship between income inequality and health outcomes.
  • A comparative analysis of the effectiveness of different weight loss programs.
  • An investigation into the factors that affect job satisfaction among employees.
  • A statistical analysis of the relationship between poverty and crime.
  • A study of the factors that influence the success of small businesses.
  • A survey of the prevalence and causes of childhood obesity.
  • An evaluation of the effectiveness of drug addiction treatment programs.
  • A statistical analysis of the relationship between gender and leadership in organizations.
  • A study of the relationship between parental involvement and academic achievement.
  • An investigation into the causes and consequences of income inequality.
  • A comparative analysis of the effectiveness of different types of therapy for mental health conditions.
  • A survey of the prevalence and causes of substance abuse among teenagers.
  • An evaluation of the effectiveness of online education compared to traditional classroom learning.
  • A statistical analysis of the impact of globalization on different industries.
  • A study of the relationship between social media use and political polarization.
  • An investigation into the factors that influence customer loyalty in the retail industry.
  • A comparative analysis of the effectiveness of different types of advertising.
  • A survey of the prevalence and causes of workplace discrimination.
  • An evaluation of the effectiveness of different types of employee training programs.
  • A statistical analysis of the relationship between air pollution and health outcomes.
  • A study of the factors that affect employee turnover rates.
  • An investigation into the causes and consequences of income mobility.
  • A comparative analysis of the effectiveness of different types of leadership styles.
  • A survey of the prevalence and causes of mental health disorders among college students.
  • An evaluation of the effectiveness of different types of cancer treatments.
  • A statistical analysis of the impact of social media influencers on consumer behavior.
  • A study of the factors that influence the adoption of renewable energy sources.
  • An investigation into the relationship between alcohol consumption and health outcomes.
  • A comparative analysis of the effectiveness of different types of conflict resolution strategies.
  • A survey of the prevalence and causes of childhood poverty.
  • An evaluation of the effectiveness of different types of diversity training programs.
  • A statistical analysis of the relationship between immigration and economic growth.
  • A study of the factors that influence customer satisfaction in the service industry.
  • An investigation into the causes and consequences of urbanization.
  • A comparative analysis of the effectiveness of different types of economic policies.
  • A survey of the prevalence and causes of elder abuse.
  • An evaluation of the effectiveness of different types of rehabilitation programs for prisoners.
  • A statistical analysis of the impact of automation on different industries.
  • A study of the factors that influence employee productivity in the workplace.
  • An investigation into the causes and consequences of gentrification.
  • A comparative analysis of the effectiveness of different types of humanitarian aid.
  • A survey of the prevalence and causes of homelessness.
  • Exploring the relationship between socioeconomic status and access to healthcare services
  • An analysis of the relationship between parental education level and children’s academic performance.
  • Exploring the effects of different statistical models on prediction accuracy in machine learning.
  • The Impact of Social Media on Consumer Behavior: A Statistical Analysis
  • Bayesian hierarchical modeling for network data analysis
  • Spatial statistics and modeling for environmental data
  • Nonparametric methods for time series analysis
  • Bayesian inference for high-dimensional data analysis
  • Multivariate analysis for genetic data
  • Machine learning methods for predicting financial markets
  • Causal inference in observational studies
  • Sampling design and estimation for complex surveys
  • Robust statistical methods for outlier detection
  • Statistical inference for large-scale simulations
  • Survival analysis and its applications in medical research
  • Mixture models for clustering and classification
  • Time-varying coefficient models for longitudinal data
  • Multilevel modeling for complex data structures
  • Graphical modeling and Bayesian networks
  • Experimental design for clinical trials
  • Inference for network data using stochastic block models
  • Nonlinear regression modeling for data with complex structures
  • Statistical learning for social network analysis
  • Time series forecasting using deep learning methods
  • Model selection and variable importance in high-dimensional data
  • Spatial point process modeling for environmental data
  • Bayesian spatial modeling for disease mapping
  • Functional data analysis for longitudinal studies
  • Bayesian network meta-analysis
  • Statistical methods for big data analysis
  • Mixed-effects models for longitudinal data
  • Clustering algorithms for text data
  • Bayesian modeling for spatiotemporal data
  • Multivariate analysis for ecological data
  • Statistical analysis of genomic data
  • Bayesian network inference for gene regulatory networks
  • Principal component analysis for high-dimensional data
  • Time series analysis of financial data
  • Multivariate survival analysis for complex outcomes
  • Nonparametric estimation of causal effects
  • Bayesian network analysis of complex systems
  • Statistical inference for multilevel network data
  • Generalized linear mixed models for non-normal data
  • Bayesian inference for dynamic systems
  • Latent variable modeling for categorical data
  • Statistical inference for social network data
  • Regression models for panel data
  • Bayesian spatiotemporal modeling for climate data
  • Predictive modeling for customer behavior analysis
  • Nonlinear time series analysis for ecological systems
  • Statistical modeling for image analysis
  • Bayesian hierarchical modeling for longitudinal data
  • Network-based clustering for high-dimensional data
  • Bayesian spatial modeling for ecological systems.
  • Analysis of the Effect of Climate Change on Crop Yields: A Case Study
  • Examining the Relationship Between Physical Activity and Mental Health in Young Adults
  • A Comparative Study of Crime Rates in Urban and Rural Areas Using Statistical Methods
  • Investigating the Effect of Online Learning on Student Performance in Mathematics
  • A Statistical Analysis of the Relationship Between Economic Growth and Environmental Sustainability
  • Evaluating the Effectiveness of Different Marketing Strategies for E-commerce Businesses
  • Identifying the Key Factors Affecting Customer Loyalty in the Hospitality Industry
  • An Analysis of the Factors Influencing Student Dropout Rates in Higher Education
  • Examining the Impact of Gender on Salary Disparities in the Workplace Using Statistical Methods
  • Investigating the Relationship Between Physical Fitness and Academic Performance in High School Students
  • Analyzing the Effect of Social Support on Mental Health in Elderly Populations
  • A Comparative Study of Different Methods for Forecasting Stock Prices
  • Investigating the Effect of Online Reviews on Consumer Purchasing Decisions
  • Identifying the Key Factors Affecting Employee Turnover Rates in the Technology Industry
  • Analyzing the Effect of Advertising on Brand Awareness and Purchase Intentions
  • A Study of the Relationship Between Health Insurance Coverage and Healthcare Utilization
  • Examining the Effect of Parental Involvement on Student Achievement in Elementary School
  • Investigating the Impact of Social Media on Political Campaigns Using Statistical Methods
  • A Comparative Analysis of Different Methods for Detecting Fraud in Financial Transactions
  • Analyzing the Relationship Between Entrepreneurial Characteristics and Business Success
  • Investigating the Effect of Job Satisfaction on Employee Performance in the Service Industry
  • Identifying the Key Factors Affecting the Adoption of Renewable Energy Technologies
  • A Study of the Relationship Between Personality Traits and Academic Achievement
  • Examining the Impact of Social Media on Body Image and Self-Esteem in Adolescents
  • Investigating the Effect of Mobile Advertising on Consumer Behavior
  • Analyzing the Relationship Between Healthcare Expenditures and Health Outcomes Using Statistical Methods
  • A Comparative Study of Different Methods for Analyzing Customer Satisfaction Data
  • Investigating the Impact of Economic Factors on Voter Behavior Using Statistical Methods
  • Identifying the Key Factors Affecting Student Retention Rates in Community Colleges
  • Analyzing the Relationship Between Workplace Diversity and Organizational Performance
  • Investigating the Effect of Gamification on Learning and Motivation in Education
  • A Study of the Relationship Between Social Support and Depression in Cancer Patients
  • Examining the Impact of Technology on the Travel Industry Using Statistical Methods
  • Investigating the Effect of Customer Service Quality on Customer Loyalty in the Retail Industry
  • Analyzing the Relationship Between Internet Usage and Social Isolation in Older Adults
  • A Comparative Study of Different Methods for Predicting Customer Churn in Telecommunications
  • Investigating the Impact of Social Media on Consumer Attitudes Towards Brands Using Statistical Methods
  • Identifying the Key Factors Affecting Student Success in Online Learning Environments
  • Analyzing the Relationship Between Employee Engagement and Organizational Commitment
  • Investigating the Effect of Customer Reviews on Sales in E-commerce Businesses
  • A Study of the Relationship Between Political Ideology and Attitudes Towards Climate Change
  • Examining the Impact of Technological Innovations on the Manufacturing Industry Using Statistical Methods
  • Investigating the Effect of Social Support on Postpartum Depression in New Mothers
  • Analyzing the Relationship Between Cultural Intelligence and Cross-Cultural Adaptation
  • Investigating the relationship between socioeconomic status and health outcomes using statistical methods.
  • Analyzing trends in crime rates and identifying factors that contribute to them using statistical methods.
  • Examining the effectiveness of different advertising strategies using statistical analysis of consumer behavior.
  • Identifying factors that influence voting behavior and election outcomes using statistical methods.
  • Investigating the relationship between employee satisfaction and productivity in the workplace using statistical methods.
  • Developing new statistical models to better understand the spread of infectious diseases.
  • Analyzing the impact of climate change on global food production using statistical methods.
  • Identifying patterns and trends in social media data using statistical methods.
  • Investigating the relationship between social networks and mental health using statistical methods.
  • Developing new statistical models to predict financial market trends and identify investment opportunities.
  • Analyzing the effectiveness of different educational programs and interventions using statistical methods.
  • Investigating the impact of environmental factors on public health using statistical methods.
  • Developing new statistical models to analyze complex biological systems and identify new drug targets.
  • Analyzing trends in consumer spending and identifying factors that influence buying behavior using statistical methods.
  • Investigating the relationship between diet and health outcomes using statistical methods.
  • Developing new statistical models to analyze gene expression data and identify biomarkers for disease.
  • Analyzing patterns in crime data to predict future crime rates and improve law enforcement strategies.
  • Investigating the effectiveness of different medical treatments using statistical methods.
  • Developing new statistical models to analyze the impact of air pollution on public health.
  • Analyzing trends in global migration and identifying factors that influence migration patterns using statistical methods.
  • Investigating the impact of automation on the job market using statistical methods.
  • Developing new statistical models to analyze climate data and predict future climate trends.
  • Analyzing trends in online shopping behavior and identifying factors that influence consumer decisions using statistical methods.
  • Investigating the impact of social media on political discourse using statistical methods.
  • Developing new statistical models to analyze gene-environment interactions and identify new disease risk factors.
  • Analyzing trends in the stock market and identifying factors that influence investment decisions using statistical methods.
  • Investigating the impact of early childhood education on long-term academic and social outcomes using statistical methods.
  • Developing new statistical models to analyze the relationship between human behavior and the environment.
  • Analyzing trends in the use of renewable energy and identifying factors that influence adoption rates using statistical methods.
  • Investigating the impact of immigration on labor market outcomes using statistical methods.
  • Developing new statistical models to analyze the relationship between social determinants and health outcomes.
  • Analyzing patterns in customer churn to predict future customer behavior and improve business strategies.
  • Investigating the effectiveness of different marketing strategies using statistical methods.
  • Developing new statistical models to analyze the relationship between air pollution and climate change.
  • Analyzing trends in global tourism and identifying factors that influence travel behavior using statistical methods.
  • Investigating the impact of social media on mental health using statistical methods.
  • Developing new statistical models to analyze the impact of transportation on the environment.
  • Analyzing trends in global trade and identifying factors that influence trade patterns using statistical methods.
  • Investigating the impact of social networks on political participation using statistical methods.
  • Developing new statistical models to analyze the relationship between climate change and biodiversity loss.
  • Analyzing trends in the use of alternative medicine and identifying factors that influence adoption rates using statistical methods.
  • Investigating the impact of technological change on the labor market using statistical methods.
  • Developing new statistical models to analyze the impact of climate change on agriculture.
  • Investigating the impact of social media on mental health: A longitudinal study.
  • A comparison of the effectiveness of different types of teaching methods on student learning outcomes.
  • Examining the relationship between sleep duration and productivity among college students.
  • A study of the factors that influence employee job satisfaction in the tech industry.
  • Analyzing the relationship between income level and health outcomes among low-income populations.
  • Investigating the effectiveness of online learning platforms for high school students.
  • A study of the factors that contribute to success in online entrepreneurship.
  • Analyzing the impact of climate change on agricultural productivity in developing countries.
  • A comparison of different statistical models for predicting stock market trends.
  • Examining the impact of sports on mental health: A cross-sectional study.
  • A study of the factors that influence employee retention in the hospitality industry.
  • Analyzing the impact of cultural differences on international business negotiations.
  • Investigating the effectiveness of different weight loss interventions for obese individuals.
  • A study of the relationship between personality traits and academic achievement.
  • Examining the impact of technology on job displacement: A longitudinal study.
  • A comparison of the effectiveness of different types of advertising strategies on consumer behavior.
  • Analyzing the impact of environmental regulations on corporate profitability.
  • Investigating the effectiveness of different types of therapy for treating depression.
  • A study of the factors that contribute to success in e-commerce.
  • Examining the relationship between social support and mental health in the elderly population.
  • A comparison of different statistical methods for analyzing complex survey data.
  • Analyzing the impact of employee diversity on organizational performance.
  • Investigating the effectiveness of different types of exercise for improving cardiovascular health.
  • A study of the relationship between emotional intelligence and job performance.
  • Examining the impact of work-life balance on employee well-being.
  • A comparison of the effectiveness of different types of financial education programs for low-income populations.
  • Analyzing the impact of air pollution on respiratory health in urban areas.
  • Investigating the relationship between personality traits and leadership effectiveness.
  • A study of the factors that influence consumer behavior in the luxury goods market.
  • Examining the impact of social networks on political participation: A cross-sectional study.
  • A comparison of different statistical methods for analyzing survival data.
  • Analyzing the impact of government policies on income inequality.
  • Investigating the effectiveness of different types of counseling for substance abuse.
  • A study of the relationship between cultural values and consumer behavior.
  • Examining the impact of technology on privacy: A longitudinal study.
  • A comparison of the effectiveness of different types of online marketing strategies.
  • Analyzing the impact of the gig economy on job satisfaction: A cross-sectional study.
  • Investigating the effectiveness of different types of education interventions for improving financial literacy.
  • A study of the factors that contribute to success in social entrepreneurship.
  • Examining the impact of gender diversity on board performance in publicly-traded companies.
  • A comparison of different statistical methods for analyzing panel data.
  • Analyzing the impact of employee involvement in decision-making on organizational performance.
  • Investigating the effectiveness of different types of treatment for anxiety disorders.
  • A study of the relationship between cultural values and entrepreneurial success.
  • Examining the impact of technology on the labor market: A longitudinal study.
  • A comparison of the effectiveness of different types of direct mail campaigns.
  • Analyzing the impact of telecommuting on employee productivity: A cross-sectional study.
  • Investigating the effectiveness of different types of retirement planning interventions for low-income individuals.
  • Analyzing the effectiveness of different educational interventions in improving student performance
  • Investigating the impact of climate change on food production and food security
  • Identifying factors that influence employee satisfaction and productivity in the workplace
  • Examining the prevalence and causes of mental health disorders in different populations
  • Evaluating the effectiveness of different marketing strategies in promoting consumer behavior
  • Analyzing the prevalence and consequences of substance abuse in different communities
  • Investigating the relationship between social media use and mental health outcomes
  • Examining the role of genetics in the development of different diseases
  • Identifying factors that contribute to the gender wage gap in different industries
  • Analyzing the effectiveness of different policing strategies in reducing crime rates
  • Investigating the impact of immigration on economic growth and development
  • Examining the prevalence and causes of domestic violence in different populations
  • Evaluating the effectiveness of different interventions for treating addiction
  • Analyzing the prevalence and impact of childhood obesity on health outcomes
  • Investigating the relationship between diet and chronic diseases such as diabetes and heart disease
  • Examining the effects of different types of exercise on physical and mental health outcomes
  • Identifying factors that influence voter behavior and political participation
  • Analyzing the prevalence and impact of sleep disorders on health outcomes
  • Investigating the effectiveness of different educational interventions in improving health outcomes
  • Examining the impact of environmental pollution on public health outcomes
  • Evaluating the effectiveness of different interventions for reducing opioid addiction and overdose rates
  • Analyzing the prevalence and causes of homelessness in different communities
  • Investigating the relationship between race and health outcomes
  • Examining the impact of social support networks on health outcomes
  • Identifying factors that contribute to income inequality in different regions
  • Analyzing the prevalence and impact of workplace stress on employee health outcomes
  • Investigating the relationship between education and income levels in different communities
  • Examining the effects of different types of technology on mental health outcomes
  • Evaluating the effectiveness of different interventions for reducing healthcare costs
  • Analyzing the prevalence and impact of chronic pain on health outcomes
  • Investigating the relationship between urbanization and public health outcomes
  • Examining the effects of different types of drugs on health outcomes
  • Identifying factors that contribute to educational attainment in different populations
  • Analyzing the prevalence and causes of food insecurity in different communities
  • Investigating the relationship between race and crime rates
  • Examining the impact of social media on political participation and engagement
  • Evaluating the effectiveness of different interventions for reducing poverty levels
  • Analyzing the prevalence and impact of stress on mental health outcomes
  • Investigating the relationship between religion and health outcomes
  • Examining the effects of different types of parenting styles on child development outcomes
  • Identifying factors that contribute to political polarization in different regions
  • Analyzing the prevalence and causes of teenage pregnancy in different communities
  • Investigating the impact of globalization on economic growth and development
  • Examining the prevalence and impact of social isolation on mental health outcomes
  • Evaluating the effectiveness of different interventions for reducing gun violence
  • Analyzing the prevalence and impact of bullying on mental health outcomes
  • Investigating the relationship between immigration and crime rates
  • Examining the effects of different types of diets on health outcomes
  • Identifying factors that contribute to social inequality in different regions
  • Bayesian inference for high-dimensional models
  • Analysis of longitudinal data with missing values
  • Nonparametric regression with functional predictors
  • Estimation and inference for copula models
  • Statistical methods for neuroimaging data analysis
  • Robust methods for high-dimensional data analysis
  • Analysis of spatially correlated data
  • Bayesian nonparametric modeling
  • Statistical methods for network data
  • Optimal experimental design for nonlinear models
  • Multivariate time series analysis
  • Inference for partially identified models
  • Statistical learning for personalized medicine
  • Statistical inference for rare events
  • High-dimensional mediation analysis
  • Analysis of multi-omics data
  • Nonparametric regression with mixed types of predictors
  • Estimation and inference for graphical models
  • Statistical inference for infectious disease dynamics
  • Robust methods for high-dimensional covariance matrix estimation
  • Analysis of spatio-temporal data
  • Bayesian modeling for ecological data
  • Multivariate spatial point pattern analysis
  • Statistical methods for functional magnetic resonance imaging (fMRI) data
  • Nonparametric estimation of conditional distributions
  • Statistical methods for spatial econometrics
  • Inference for stochastic processes
  • Bayesian spatiotemporal modeling
  • High-dimensional causal inference
  • Analysis of data from complex survey designs
  • Bayesian nonparametric survival analysis
  • Statistical methods for fMRI connectivity analysis
  • Spatial quantile regression
  • Statistical modeling for climate data
  • Estimation and inference for item response models
  • Bayesian model selection and averaging
  • High-dimensional principal component analysis
  • Analysis of data from clinical trials with noncompliance
  • Nonparametric regression with censored data
  • Statistical methods for functional data analysis
  • Inference for network models
  • Bayesian nonparametric clustering
  • High-dimensional classification
  • Analysis of ecological network data
  • Statistical modeling for time-to-event data with multiple events
  • Estimation and inference for nonparametric density estimation
  • Bayesian nonparametric regression with time-varying coefficients
  • Statistical methods for functional magnetic resonance spectroscopy (fMRS) data

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Statistics Research Topics: Ideas & Questions

June 16, 2023

Looking for research topics in statistics? Whether you’re a student working on a class project or a researcher in need of inspiration, finding the right topic can be challenging. With numerous areas to explore in statistics, narrowing down your options can be overwhelming. But with some creativity and research, you can find an interesting and relevant topic. This article offers ideas and examples of statistics research topics to consider, so let’s dive in!

Statistics Research: What It Comprises

The data collected by statistics research can be quantitative (numbers) or qualitative (text). The data can also be presented in tables or graphs for easy understanding by the audience. However, it is not always necessary to present the data in the form of tables or graphs, as sometimes the raw data can be good enough to convey the message from the researcher.

In statistics projects, the researchers usually design experiments to test specific hypotheses about a population’s characteristics or behavior. For example, suppose you want to know whether people who wear glasses will have better eyesight than those who don’t wear glasses. In that case, you need to collect information about their vision before and after wearing glasses (experimental group) and compare their vision with those who do not wear glasses (control group). You would then find out whether there was any difference between these two groups with respect to eyesight improvement due to wearing glasses.

Tips on How to Choose a Statistics Research Topic

Firstly, remember that a good statistics topic should interest you and also have a substantial amount of data available for analysis. Once you have decided on your topic, you can collect data for your study using secondary sources or conducting primary research through surveys or interviews.

You can also use search engines like Google or Yahoo! to find information about your topic of interest. You can use keywords like “income disparity” or “inequality causes” to find relevant websites on which you can find information related to your topic of interest.

Next, consider what types of questions your supervisor would like answered with this data type. For example, if you’re looking at crime rates in your city, maybe they would like to know which areas have higher crime rates than others to plan police patrols accordingly. Or maybe they just want to know whether there’s any correlation between high crime rates and low-income neighborhoods (there probably will be).

Statistics Research Topics in Business

  • Understanding the factors that influence consumer purchase decisions in the technology industry
  • Advertising and sales revenue: a time-series analysis
  • The effectiveness of customer loyalty programs in increasing customer retention and revenue
  • The relationship between employee job satisfaction and productivity
  • The factors that contribute to employee turnover in the hospitality industry
  • Product quality on customer satisfaction and loyalty: a longitudinal study
  • The application of social media marketing in increasing brand awareness and customer engagement
  • Corporate social responsibility (CSR) initiatives and brand reputation: a meta-analysis
  • Understanding the factors that influence customer satisfaction in the restaurant industry
  • E-commerce on traditional brick-and-mortar retail sales: a comparative analysis
  • The effectiveness of supply chain management strategies in reducing operational costs and improving efficiency
  • The relationship between market competition and innovation: a cross-country analysis
  • Understanding the factors that influence employee motivation and engagement in the workplace
  • Business analytics on strategic decision-making: a case study approach
  • The effectiveness of performance-based incentives in increasing employee productivity and job satisfaction
  • Organizational performance dependence on employee diversity and organizational performance
  • Understanding the factors that contribute to startup success in the technology industry
  • The impact of pricing strategies on sales revenue and profitability
  • The effectiveness of corporate training programs in improving employee skill development and performance
  • The relationship between brand image and customer loyalty

Research Topics in Applied Statistics

  • The impact of educational attainment on income level
  • The effectiveness of different advertising strategies in increasing sales
  • The relationship between socioeconomic status and health outcomes
  • The effectiveness of different teaching methods in promoting academic success
  • The impact of job training programs on employment rates
  • The relationship between crime rates and community demographics
  • Different medication dosages in treating a particular condition
  • The influence of environmental pollutants on health outcomes
  • The interconnection between access to healthcare and health outcomes
  • The effectiveness of different weight loss programs in promoting weight loss
  • The impact of social support on mental health outcomes
  • The relationship between demographic factors and political affiliation
  • The effectiveness of different exercise programs in promoting physical fitness
  • The influence of parenting styles on child behavior
  • The relationship between diet and chronic disease risk
  • Different smoking cessation programs for promoting smoking cessation
  • The impact of public transportation on urban development
  • The relationship between technology usage and social isolation
  • The effectiveness of different stress reduction techniques in reducing stress levels
  • The influence of climate change on crop

Statistics Research Topics in Psychology

  • The correlation between childhood trauma and adult depression
  • The effectiveness of cognitive-behavioral therapy in treating anxiety disorders
  • The impact of social media on self-esteem and body image in adolescents
  • Personality traits and job satisfaction: how are they related?
  • The prevalence and predictors of bullying in schools
  • The effects of sleep deprivation on cognitive performance
  • The role of parenting styles in the development of emotional intelligence
  • The effectiveness of mindfulness-based interventions in reducing stress and anxiety
  • The impact of childhood abuse on adult relationship satisfaction
  • The influence of social support on coping with chronic illness
  • The factors that contribute to successful aging
  • The prevalence and predictors of addiction relapse
  • The impact of cultural factors on mental health diagnosis and treatment
  • Exercise and mental health: in which way are they connected?
  • The effectiveness of art therapy in treating trauma-related disorders
  • The prevalence and predictors of eating disorders in college students
  • The influence of attachment styles on romantic relationships
  • The effectiveness of group therapy in treating substance abuse disorders
  • The prevalence and predictors of postpartum depression
  • The impact of childhood socioeconomic

Sports Statistics Research Topics

  • The relationship between player performance and team success in the National Football League (NFL)
  • Understanding the factors that influence home-field advantage in professional soccer
  • The impact of game-day weather conditions on player performance in Major League Baseball (MLB)
  • The effectiveness of different training regimens in improving endurance and performance in long-distance running
  • The relationship between athlete injury history and future injury risk in professional basketball
  • The impact of crowd noise on team performance in college football
  • The effectiveness of sports psychology interventions in improving athlete performance and mental health
  • The relationship between player height and success in professional basketball: a regression analysis
  • Understanding the factors that contribute to the development of youth soccer players in the United States
  • The influence of playing surface on injury rates in professional football: a longitudinal study
  • The effectiveness of pre-game routines in improving athlete performance in tennis
  • The relationship between athletic ability and academic success among college athletes
  • Understanding the factors that influence injury risk and recovery time in professional hockey players
  • The impact of in-game statistics on coaching decisions in professional basketball
  • The effectiveness of different dietary regimens in improving athlete performance in endurance sports
  • The relationship between athlete sleep habits and performance: a longitudinal study
  • Understanding the factors that influence athlete endorsement deals and sponsorships in professional sports
  • The influence of stadium design on crowd noise levels and player performance in college football
  • The effectiveness of different strength training regimens in improving athlete performance in track and field events
  • The relationship between player salary and team success in professional baseball: a longitudinal analysis

Survey Methods Statistics Research Topics

  • Understanding the factors that influence response rates in online surveys
  • The effectiveness of different survey question formats in eliciting accurate and reliable responses
  • The relationship between survey mode (phone, online, mail) and response quality in political polling
  • The impact of incentives on survey response rates and data quality
  • Understanding the factors that contribute to respondent satisfaction in surveys
  • The effectiveness of different sampling methods in achieving representative samples in survey research
  • The relationship between survey item order and response bias: a meta-analysis
  • The impact of social desirability bias on survey responses: a longitudinal study
  • Understanding the factors that influence survey question wording and response bias
  • The effectiveness of different visual aids in improving respondent comprehension and response quality
  • The relationship between survey timing and response rate: a comparative analysis
  • The impact of interviewer characteristics on survey response quality in face-to-face surveys
  • Understanding the factors that contribute to nonresponse bias in survey research
  • The effectiveness of different response scales in measuring attitudes and perceptions in surveys
  • The relationship between survey length and respondent engagement: a cross-sectional analysis
  • The influence of skip patterns on survey response quality and completion rates
  • Understanding the factors that influence survey item nonresponse and item refusal rates
  • The effectiveness of pre-testing and piloting surveys in improving data quality and reliability
  • The relationship between survey administration and response quality: a comparative analysis of phone, online, and in-person surveys
  • The impact of survey fatigue on response quality and data completeness: a longitudinal study

As mentioned above, statistics is the science of collecting and analyzing data to draw conclusions and make predictions. To conduct a proper statistical analysis, you must first define your research question, gather data from various sources, analyze the information, and draw conclusions based on the results.

This process can be challenging for many people who do not have an extensive background in statistics. However, it does not have to be so tricky if you use our professional Custom Writing help. Our writers are highly qualified professionals who will work with you to develop a clear understanding of your research problem and then guide you through every step of the process. We will also ensure that your paper follows all academic standards to meet all requirements for originality and quality.

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150 Best Statistics Research Paper Topics for You

Statistics Research Topics

Are you taking stats and want to get top grades in assignments? The two most important things are getting good topics and writing the papers professionally. Unfortunately, these two steps are very challenging and make some students score low or failing grades. However, we are here to help. Keep reading to see our list of top 150 leading stats research topics and pick the preferred option.

Stats Research Topics in Sports

  • Abuse of drugs in college sports clubs: Statistical evaluation of doping in the last ten years.
  • A review of the evidence of traumatic head injuries in soccer and baseball.
  • Olympics competitions: Interesting statistical observations since the start of the game.
  • Analysis of knee injuries in the UK soccer league.
  • Comparing the statistical data on leg injuries in the Spanish and UK football leagues.
  • Gender balance in sports clubs management: Comparing the managements of basketball in the US and Europe.
  • Comparing the statistics of soccer professionals in Europe to those in the US.
  • Linking staff training to good performance in soccer: A statistical review.
  • Numbers do not lie: A statistical review of the performance of soccer professionals from Africa to those from Europe.
  • A statistical review of success factors in indoor sports.
  • Probability application in sports betting: Comparing top two betting sites.
  • A review of life success for soccer professionals after retiring.
  • Does the amount paid to professional athletes determine their rate of success in the field?
  • Do temperatures impact the performance of soccer players in Europe?

Hop Topics Research Topics for Statistics Students

  • A comprehensive analysis of non-experimental correlational designs in statistics.
  • The Pearson correlation and linear regression.
  • Statistical analysis of traffic peak times in London.
  • Z-test and independent T-tests: A closer look at the main assumptions and calculations.
  • A review of the effectiveness of the 2-way ANOVA on SPSS application.
  • A feasibility study of opening an electric car repair shop in New York.
  • Interpreting T-Test and Chi-Square Analyses.
  • Financial distress in the banking sector: What are the main contributors?
  • Cash deposit patterns in California banks: A statistical review.
  • Are members of specific races likely to get incarcerated than others?
  • Statistical analysis of students’ attitudes to sex in American Universities.
  • Patients with private insurance in the UK: Do they get better healthcare?
  • Decision making during disasters: A review of the most important information used by managers.
  • The impacts of Israel-Palestine conflict on the society: A statistical review of the main strategies adopted to address it so far.
  • Debt reduction strategies adopted by the government: Can they be useful in improving quality of life?
  • A statistical analysis of the effects of federal elections on the stock market?
  • Noise pollution: A comprehensive review of how it hurts the human system.
  • Global warming: What do you think of data provided by the United Nations Framework Convention on Climate Change ( UNFCCC )?

Statistical Research Topics for College Students

  • Analyzing the amount of time that college students in the UK spend on social media.
  • A statistical review of students eating habits in France.
  • What is the percentage of college students who get married within three years of completing their studies?
  • Plagiarism in college education: Statistical analysis of students who plagiarize their work.
  • Analyze the impacts of bullying on teenagers: Is it the most severe threat facing young people today?
  • Evaluate the impacts of overpopulation on small countries.
  • Energy drinks are harmful: A statistical analysis of the level of awareness among the young people.
  • Outlier removal in statistical data.
  • What were the leading causes of the Great Depression of 130?
  • Least median of squares models.
  • Suggest an alternative method of estimating regression line.
  • Time-linear coordinates triangle.
  • Do students who get involved in sports score poor grades?
  • Do the types of shoes worn by an athlete determine his/her overall performance?
  • Are the factors that make people to perform well in tennis the same as soccer? A comparative study.

Good Statistics Research Topics in Psychology

  • Prevalence of obesity in children and adults: A statistical evaluation.
  • A comparative analysis of the main causes of high rates of divorce in the US and UK.
  • Boosting a child’s self-esteem: What role does the parent play?
  • Factors that fuel suicidal thoughts in people with depression: Presenting statistical evidence.
  • A statistical review of the impacts of divorce on children.
  • Is marijuana effective in treating mental disorders? Analysis of emerging data.
  • A statistical analysis of the effectiveness of social work practice interventions in addressing dementia.
  • Childhood abuse survivors: A statistical review of how they cope at adulthood?
  • Dioxin: How does it impact cancer development in children?
  • Dug addition increase in colleges: A review of the main causes.

Interesting Statistical Research Topics for You

  • A closer look at the history of statistics and probability.
  • Analyzing the different schools of thoughts applied in statistics.
  • A deeper look at the Central Limit Theorem.
  • Least squares model.
  • Rumors, opinions and facts.
  • Levels of measurement and types data.
  • A regression analysis on the impact of drinking, levels of exercise and weight on medical costs.
  • Cluster analysis on the impact of dollar increment on the economy of Hong Kong.
  • A statistical analysis of the US federal administration’s expenditure and revenue.
  • A statistical study of the impacts of agricultural loans on faming in Sri Lanka.
  • A statistical review of reasons that makes students to select the courses to pursue in college.
  • How to effectively tackle age diversity issues at the workplace: A comparative review of strategies used by two companies of your choice in the US.
  • What is the most preferred meal by students when away from college?
  • A statistical analysis of data on the effects of taking a back or front seats on learners’ grades.

Experimental Statistical Research Papers Ideas

  • A comparative assessment of changes in wealth distribution among African Americans and Hispanics.
  • Infant mortality rates: What are the main causes of high rates in developing countries?
  • Life expectancy of adult female alcoholics vs adult male alcoholics: A comparative study.
  • Why do long-term smokers find it very tough to quit smoking?
  • Stochastic multi-agent model in financial markets: A statistical review.
  • A statistical review of investors’ behavior in the bearish market?
  • Causes of business failure within the first five years: A comparative review of startups in the UK and US markets.
  • Statistical reviews of the factors that make foreigners choose Hong Kong and Singapore as the leading offshore investment hub.
  • Analyzing the effectiveness of the World Bank’s ease of doing business index calculation.
  • Which gender is more helpful in economy building? A comparative review.
  • The application of time series in business.
  • Analyzing the relationship between holding positions in business and success in personal life.
  • A statistical review of the rates of crime in the Caribbean.
  • What is the level of visitor satisfaction when traveling to Italy?

Statistics Research Topics in Business

  • Do more female employees experience higher levels of harassment compared to male colleagues?
  • Comparing accessibility to bank loans in the US and a developing country of choice.
  • Are Swedish people more direct when it comes to doing business away from home? Build your ideas from famous Swedish businesspeople.
  • Does social media influence impact the staff turnover in international companies? A statistical review.
  • Alcohol consumption data review: Is it higher among employees with lower or high pay scales?
  • Comparing debt management strategies used by Singaporeans to those of Americans: A statistical analysis.
  • A review of data on the main threats facing businesses in the UK today.
  • The latest e-commerce trends on the Globe: What do they say about the future of the retail industry?
  • Analyzing the main factors that cause low productivity among industrial employees.
  • A statistical evaluation of the impact of workplace appraisals in the banking industry.
  • Analyzing the relationship between production system of an organization and profitability: A case study of the soft drink industry in the US.
  • Cost-volume-profit analysis: Is it a useful tool when working on improving decision making in organizations.
  • Adoption of modern communication equipment: What impact does it have on employee performance?
  • The six sigma quality.

Unique Statistics Research Paper Topics

  • The statistical reasons for the high popularity of Christianity in the 20th century.
  • Real markets and fast growth of online trade: A statistical review of causes and effects.
  • Analyzing how the mindsets of past generations have influenced the modern teaching methods.
  • Statistic is resistant: What does the phrase mean?
  • Chebyshev’s inequality: Does it apply to all distributions apart from shapes?
  • Observational versus designed experiment: What are the differences?
  • What is the impact of petroleum prices on agricultural food prices?
  • Discriminant and classification.
  • What is the relationship between poverty and crime rates?
  • A statistical evaluation of types of crime recorded in your city.
  • A statistical analysis of the causes of road accidents in your state.
  • Is there a relationship between income per capita and medical expenses?
  • Analysis of the main sources of revenue and expenditure patterns in the US.
  • Moderation and mediation.
  • Is the word really getting warm? A statistical review?
  • Information theory.
  • Multiple comparison tests.
  • Statistical data analysis in criminal justice.

Awesome Statistics Research Questions

  • What is t he Black – Scholes model?
  • Sampling distribution: What are the main applications?
  • Intelligent numerical computation: How does it work?
  • What are the merits and demerits of stem-and-leaf plots versus histogram?
  • What are the advantages of using 2-scores in the comparison of observations of two preferred data sets?
  • Calculation of the sample standard deviation: What do the degrees of freedom mean?
  • Tests for normality.
  • Analyzing the trends of domestic investment in France (2011-2020).
  • Entropy measure.
  • Nuclear disasters: What has the world learnt from them?
  • Does caffeine use impact the student’s performance in college?
  • What are the main types of data and data measurements?
  • Bias reduction strategies: How do they work?
  • NBA professional sportspersons: Why do they earn so much?
  • Does the race of actors determine the success of TV shows in the US?
  • Birth disorders and success in academics: Are they related?

Medicine Statistical Research Topic

  • Evaluating the impact of methamphetamine on substance abusers: A statistical approach.
  • A statistical analysis of the effectiveness of chemotherapy in treating breast cancer for women over 40 years.
  • Comparing the impact of breastfeeding to the use of formula milk on a child’s cognitive development.
  • Orthodox therapies versus alternative therapies application in the treatment of cancer: A comparative assessment.
  • What factors can help reduce the risk of teenage pregnancies: A statistical analysis.
  • Genetic engineering and cloning: What is the probability of the two becoming the norm in the future?
  • Ethics in medicine: Can results from unethical experiments be applied to save other people?
  • Statistical analysis of mental disorders in the UK.
  • A comparative study of typical healthy problems at puberty.

Research Topic for Statistics Project

  • Why we should break stereotypes in the society: Identify one stereotype and break it in your final project.
  • Probiotics: A statistical review of their impacts on gastrointestinal system of people in Latin America.
  • The safest and most dangerous neighborhoods in your state.
  • Should art be given the same importance to sciences? A comparative study.
  • Child marriage should be stopped: A statistical review of the benefits of delayed marriage in girls.
  • Does academic success guarantee a successful life?
  • Noise pollution vs physical pollution: A comparative study to determine which is more harmful?
  • What type of music is more popular among college students?

When to Seek Writing Help

After reading through the best statistics research ideas, have you selected your preferred title? Whether you were looking for statistics project ideas or title for a great paper, one thing to appreciate is that this is only the beginning. The longer journey, which many students find very challenging, is writing the papers after picking statistics topics.

Most of them indicate that statistics is a challenging area while others lack time to work on the assignments. The good news is that getting paid help online is easy, cheap and fast. We have experts in statistics who are ready to provide students who need help any time of the day or night. No matter the selected statistics survey topics, you are guaranteed of professionally done papers. Let an expert hold your hand for top grades!

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120 Statistical Research Topics: Explore Up-to-date Trends

Statistical Research Topics Latest Trends & Techniques

Researchers and statistics teachers are often tasked with writing an article or paper on a given stats project idea. One of the most crucial things in writing an outstanding and well-composed statistics research project, paper, or essay is to come up with a very interesting topic that will captivate your reader’s minds and provoke their thoughts.

What Are the Best Statistical Research Topics Worth Writing On?

Leading statistical research topics for college students that will interest you, project topics in statistics worth considering, the best idea for statistics project you can focus on, good experiments for statistics topics you should be writing on, what are the best ap statistics project ideas that will be of keen interest to you, good statistics project ideas suitable for our modern world, some of the most crucial survey topics for statistics project, statistical projects topics every researcher wants to write on, statistical research topics you can focus your research on.

Students often find it difficult to come up with well-composed statistical research project topics that take the format of argumentative essay topics to pass across their message. In this essay, we will look at some of the most interesting statistics research topics to focus your research on.

Here are some of the best statistical research topics worth writing on:

  • Predictive Healthcare Modeling with Machine Learning
  • Analyzing Online Education During COVID-19 Epidemic
  • Modeling How Climate Change Affects Natural Disasters
  • Essential Elements Influencing Personnel Productivity
  • Social Media Influence on Customer Choices and Behavior
  • Can Geographical Statistics Aid In Analyzing Crime Trends and Patterns?
  • Financial Markets and Stock Price Predictions
  • Statistical Analysis of Voting-related Behaviors
  • An Analysis of Public Transportation Usage Trends in Urban Areas
  • How Can Public Health Education Reduce Air Pollution?
  • Statistical Analysis of Suicide In Adolescents and Adults
  • A Review of Divorce and How It Affects Children

As a college student, here are the best statistical projects for high school students to focus your research on, especially if you need social media research topics .

  • Major Factors Influencing College Students’ Academic Performance
  • Social Media and How It Defines thee Mental Health of Students
  • Evaluation of the Elements Influencing Student Engagement and Retention
  • An Examination of Extracurricular Activities On Academic Success
  • Does Parental Involvement Determine Academic Achievement of Kids?
  • Examining How Technology Affects Improving Educational Performance
  • Factors That Motivate Students’ Involvement In Online Learning
  • The Impact of Socioeconomic Status On Academic Performance
  • Does Criticism Enhance Student Performance?
  • Student-Centered Learning and Improved Performance
  • A Cursory Look At Students’ Career Goals and Major Life Decisions
  • Does Mental Health Impact Academic Achievement?

Are you a student tasked with writing a project but can’t come up with befitting stats research topics? Here are the best ideas for statistical projects worth considering:

  • Financial Data And Stock Price Forecasting
  • Investigation of Variables Influencing Students’ Grades
  • What Causes Traffic Flow and Congestion In Urban Areas?
  • How to Guarantee Customer Retention In the Retail Sector
  • Using Epidemiological Data to Model the Spread of Infectious Diseases
  • Does Direct Advertisement Affect Consumer Preferences and Behavior?
  • How to Predict and Adapt to Climate Change
  • Using Spatial Statistics to Analyze Trends and Patterns In Crime
  • Examination of the Elements Influencing Workplace Morale and Productivity
  • Understanding User Behavior and Preferences Through Statistical Analysis of Social Media Data
  • How Many Percent Get Married After Their Degree Programs?
  • A Comparative Analysis of Different Academic Fee Payments

If you have been confused based on the availability of different statistics project topics to choose from, here are some of the best thesis statement about social media to choose from:

  • Analysis of the Variables Affecting A Startup’s Success
  • The Valid Connection Between Mental Health and Social Media Use
  • Different Teaching Strategies and Academic Performance
  • Factors Influencing Employee Satisfaction In Different Work Environments
  • The Impact of Public Policy On Different Population Groups
  • Reviewing Different Health Outcomes and Incomes
  • Different Marketing Tactics for Good Service Promotion
  • What Influences Results In Different Sports Competitions?
  • Differentiating Elements Affecting Students’ Performance In A Given Subject
  • Internal Communication and Building An Effective Workplace
  • Does the Use of Business Technologies Boost Workers’ Output?
  • The Role of Modern Communication In An Effective Company Management

Are you a student tasked with writing an essay on social issues research topics but having challenges coming up with a topic? Here are some amazing statistical experiments ideas you can center your research on.

  • How Global Pandemic Affects Local Businesses
  • Investigating the Link Between Income and Health Outcomes In a Demography
  • Key Motivators for Student’s Performance In a Particular Academic Program
  • Evaluating the Success of a Promotional Plan Over Others
  • Continuous Social Media Use and Impact On Mental Health
  • Does Culture Impact the Religious Beliefs of Certain Groups?
  • Key Indicators of War and How to Manage These Indicators
  • An Overview of War As a Money Laundering Scheme
  • How Implementations Guarantee Effectiveness of Laws In Rural Areas
  • Performance of Students In War-torn Areas
  • Key Indicators For Measuring the Success of Your Venture
  • How Providing FAQs Can Help a Business Scale

The best AP statistic project ideas every student especially those interested in research topics for STEM students  will want to write in include:

  • The Most Affected Age Demography By the Covid-19 Pandemic
  • The Health Outcomes Peculiar to a Specific Demography
  • Unusual Ways to Enhance Student Performance In a Classroom
  • How Marketing Efforts Can Determine Promotional Outputs
  • Can Mental Health Solutions Be Provided On Social Media?
  • Assessing How Certain Species Are Affected By Climate Change.
  • What Influences Voter Turnouts In Different Elections?
  • How Many People Have Used Physical Exercises to Improve Mental Health
  • How Financial Circumstances Can Determine Criminal Activities
  • Ways DUI Laws Can Reduce Road Accidents
  • Examining the Connection Between Corruption and Underdevelopment In Africa
  • What Key Elements Do Top Global Firms Engage for Success?

If you need some of the best economics research paper topics , here are the best statistics experiment ideas you can write research on:

  • Retail Client Behaviors and Weather Trends
  • The Impact of Marketing Initiatives On Sales and Customer Retention
  • How Socioeconomic Factors Determine Crime Rates In Different Locations
  • Public and Private School Students: Who Performs Better?
  • How Fitness Affects the Mental Health of People In Different Ages
  • Focus On the Unbanked Employees Globally
  • Does Getting Involve In a Kid’s Life Make Them Better?
  • Dietary Decisions and a Healthy Life
  • Managing Diabetes and High Blood Pressure of a Specific Group
  • How to Engage Different Learning Methods for Effectiveness
  • Understudying the Sleeping Habits of Specific Age Groups
  • How the Numbers Can Help You Create a Brand Recognition

As a student who needs fresh ideas relating to the topic for a statistics project to write on, here are crucial survey topics for statistics that will interest you.

  • Understanding Consumer Spending and Behavior In Different Regions
  • Why Some People in Certain Areas Live Longer than Others
  • Comparative Analysis of Different Customer Behaviors
  • Do Social Media Businesses Benefit More than Physical Businesses?
  • Does a Healthy Work Environment Guarantee Productivity?
  • The Impact of Ethnicity and Religion On Voting Patterns
  • Does Financial Literacy Guarantee Better Money Management?
  • Cultural Identities and Behavioral Patterns
  • How Religious Orientation Determines Social Media Use
  • The Growing Need for Economists Globally
  • Getting Started with Businesses On Social Media
  • Which Is Better: A 9-5 or An Entrepreneurial Job?

Do you want to write on unique statistical experiment ideas? Here are some topics you do not want to miss out on:

  • Consumer Satisfaction-Related Variables on E-Commerce Websites
  • Obesity Rates and Socioeconomic Status In Developed Countries
  • How Marketing Strategies Can Make or Mar Sales Performance
  • The Correlation Between Increased Income and Happiness In Various Nations
  • Regression Models and Forecasting Home Prices
  • Climate Change Affecting Agricultural Production In Specific Areas
  • A Study of Employee Satisfaction In the Healthcare Industry
  • Social Media, Marketing Tactics, and Consumer Behavior In the Fashion Industry
  • Predicting the Risk of Default Among Credit Card Holders In Different Regions
  • Why Crime Rates Are Increasing In Urban Areas than Rural Areas
  • Statistical Evaluation of Methamphetamine’s Impact On Drug Users
  • Genes and a Child’s Total Immunity

Here are some of the most carefully selected stat research topics you can focus on.

  • Social Media’s Effects On Consumer Behavior
  • The Correlation Between Urban Crime Rates and Poverty Levels
  • Physical Exercise and Mental Health Consequences
  • Predictive Modeling In the Financial Markets
  • How Minimum Wage Regulations Impact Employment Rates
  • Healthcare Outcomes and Access Across Various Socioeconomic Groups
  • How High School Students’ Environment Affect Academic Performance
  • Automated Technology and Employment Loss
  • Environmental Elements and Their Effects On Public Health
  • Various Advertising Tactics and How They Influence Customer Behavior
  • Political Polarization And Economic Inequality
  • Climate Change and Agricultural Productivity

The above statistics final project examples will stimulate your curiosity and test your abilities, and they can even be linked to some biochemistry topics and anatomy research paper topics . Writing about these statistics project ideas helps provide a deeper grasp of the natural and social phenomena that affect our lives and the environment by studying these subjects.

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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

  • Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
  • Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
  • Null hypothesis: Parental income and GPA have no relationship with each other in college students.
  • Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

  • In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
  • In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
  • In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

  • In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
  • In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
  • In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
  • Experimental
  • Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

  • Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
  • Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

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In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

  • Probability sampling: every member of the population has a chance of being selected for the study through random selection.
  • Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

  • your sample is representative of the population you’re generalizing your findings to.
  • your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

  • Will you have resources to advertise your study widely, including outside of your university setting?
  • Will you have the means to recruit a diverse sample that represents a broad population?
  • Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

  • Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
  • Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
  • Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
  • Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

  • Organizing data from each variable in frequency distribution tables .
  • Displaying data from a key variable in a bar chart to view the distribution of responses.
  • Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

Mean, median, mode, and standard deviation in a normal distribution

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

  • Mode : the most popular response or value in the data set.
  • Median : the value in the exact middle of the data set when ordered from low to high.
  • Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

  • Range : the highest value minus the lowest value of the data set.
  • Interquartile range : the range of the middle half of the data set.
  • Standard deviation : the average distance between each value in your data set and the mean.
  • Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

  • Estimation: calculating population parameters based on sample statistics.
  • Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

  • A point estimate : a value that represents your best guess of the exact parameter.
  • An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

  • A test statistic tells you how much your data differs from the null hypothesis of the test.
  • A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

  • Comparison tests assess group differences in outcomes.
  • Regression tests assess cause-and-effect relationships between variables.
  • Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

  • A simple linear regression includes one predictor variable and one outcome variable.
  • A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

  • A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
  • A z test is for exactly 1 or 2 groups when the sample is large.
  • An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

  • If you have only one sample that you want to compare to a population mean, use a one-sample test .
  • If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
  • If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
  • If you expect a difference between groups in a specific direction, use a one-tailed test .
  • If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

  • a t value (test statistic) of 3.00
  • a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

  • a t value of 3.08
  • a p value of 0.001

The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Student’s  t -distribution
  • Normal distribution
  • Null and Alternative Hypotheses
  • Chi square tests
  • Confidence interval

Methodology

  • Cluster sampling
  • Stratified sampling
  • Data cleansing
  • Reproducibility vs Replicability
  • Peer review
  • Likert scale

Research bias

  • Implicit bias
  • Framing effect
  • Cognitive bias
  • Placebo effect
  • Hawthorne effect
  • Hostile attribution bias
  • Affect heuristic

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  • Budget of the United States Government Issued by the Office of Management and Budget (OMB), the Budget of the United States Government is a collection of documents that contains the budget message of the President, information about the President's budget proposals for a given fiscal year, and other budgetary publications that have been issued throughout the fiscal year
  • Economic Census Conducted by the U.S. Census Bureau, the Economic Census is the U.S. Government's official five-year measure of American business and the economy.
  • Consumer Price Index Produces monthly data on changes in the prices paid by urban consumers for a representative basket of goods and services.
  • Data & Statistics From the California Department of Education , this site contains data and statistics collected from California schools and learning support resources to identify trends and educational needs and to measure performance
  • National Center for Education Statistics The National Center for Education Statistics (NCES) is the primary federal entity for collecting and analyzing data related to education. Compiles stats from early childhood education through post-secondary education.
  • Fast Facts This National Center for Education Statistics page provides quick statistical answers to basic questions about education, such as "Are American students performing better in reading now than in the past?" and "How many educational institutions exist in the U.S.?"
  • American Association of Community Colleges This site offers a wide variety of information on community colleges, students, and faculty and staff.
  • Common Core of Data A program of the U.S. Department of Education's National Center for Education Statistics, this site provides annual and national statistics for all public elementary and secondary schools, and school districts across the U.S. Data can be located under "Quick Facts", "Data", or by searching for a specific school or school district under "School/District Locator." Fiscal and Nonfiscal reports, and working papers can be located under "Publications"
  • Nation's Report Card The Nation's Report Card presents the results of the National Assessment of Educational Progress (NAEP), which measures student achievement in the U.S. in various subjects over time.
  • Education (United States Census Bureau) Data from the U.S. Census Bureau.
  • MyEnvironment This U.S. Environmental Protection Agency site allows you to search by zip, city or county for environmental information on air quality, water, and hazardous waste
  • Envirofacts Data Warehouse From the U.S. Environmental Protection Agency, this site helps you find facts about air, water, land, waste, toxins, and radiation for specific areas
  • U.S. Energy Information Administration The EIA provides independent analysis and statistics on energy use: petroleum, natural gas, electricity, coal, nuclear and alternative fuels (hydro power, solar, wind, geothermal, ethanol, etc.)
  • EarthTrends: Environmental Information EarthTrends is a free on-line resource that highlights the environmental, social, and economic trends that shape our world. The site offers the public a comprehensive collection of vital statistics, maps, and graphics for more than 200 countries
  • Center for International Earth Science Information Network CIESIN’s mission is to provide access to and enhance the use of information worldwide, advancing understanding of human interactions in the environment and serving the needs of science and public and private decision making
  • Environmental Health From the Centers for Disease Control and Prevention, this site provides an alphabetically-arranged list of environmental health topics
  • National Center for Health Statistics This division of the Centers for Disease Control and Prevention gathers statistics on all manner of health topics, from aging and birth rates to diseases and physical activity. The place to start looking for health statistics
  • State Health Facts Data on demographics, health, health policy, financing, health coverage, access and state legislation; data sources are indicated. A project of the Henry J. Kaiser Family Foundation
  • Statistics From the California Department of Public Health , this site provides statistical and data tables
  • Diseases & Conditions From the California Department of Public Health , this site provides links to information for communicable, infectious, and chronic diseases. Links include fact sheets and disease information provided by CDPH as well as external agencies, organizations, and groups
  • Centers for Disease Control and Prevention Data and statistics for both diseases and other health conditions. Choose from a list of topics or use search box in upper right hand corner (for example, search on "valley fever statistics")
  • National Institutes of Health Use the search box in the upper right hand side to search for statistics across 27 different types of health institutes. For example, search on "breast cancer statistics" or search on "obesity statistics."
  • Partners in Information Access for the Public Health Workforce A collaboration of U.S. government agencies, public health organizations and health sciences libraries; features county and local, state, individual, national, and global health data.
  • Data and Statistics From the World Health Organization, data and analysis on health around the world arranged by topic
  • Morbidity and Mortality Weekly Report Weekly reports providing analysis and hard statistics on fatal diseases and causes of death. Some articles in Adobe Acrobat format
  • Cancer Statistics From the Surveillance Epidemiology and End Results of the National Cancer Institute
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Topics and trends in current statistics education research: International perspectives (front matter)

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2019, Topics and trends in current statistics education research: International perspectives

How opportune to have available in this one volume chapters highlighting rich international collaborative research in statistics education to examine the teaching and learning of statistics. It is especially relevant that much of this current research focuses on content, pedagogy, and learning at the school level and with teacher preparation. More often than not in the past and even currently, only the most able students have been encouraged to study statistics. However, we live in a constantly changing world that is structured around many different forms of data (traditional and non-traditional) that impact every individual daily. It is more urgent than ever that ALL students begin at a young age in school to develop data sense and statistical thinking. For this to happen, our school-level teachers and teacher educators must also develop data sense, conceptual understanding, and habits of mind for reasoning statistically. We need evolving research that investigates and understands...

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Statistics is taught in mathematics courses in all school levels. We suggest that using rich tasks in statistics can develop statistical reasoning and create both intra and interdisciplinary links in students. In this paper, we present three case studies where middle school mathematics teachers used different tasks in lessons on pie charts. We analyzed the actions implemented/performed/attempted by teachers to support the development of statistical reasoning and the creation of intra and interdisciplinary links in their lessons. Results show that their procedural vision of statistics led them to focus more on graphical representation, neglecting aspects of statistical reasoning. Results also reveal an interdisciplinary intersection between mathematics and statistics, which may prevent the development of statistical reasoning. First published November 2016 at Statistics Education Research Journal Archives

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The idea that the teaching of statistics should emphasise the understanding of statistical concepts and methods, and also encourage the students to acquire awareness of how important it is to be able to assess the mass of statistical information they come across every day has a similar consensus. The achievement of these aims is facilitated only if students, in their learning experience, are asked to handle concrete problems based on real settings. In that respect, a relevant contribution has been given by David Moore (1997), who suggests that &quot;the most effective learning takes place when content, pedagogy and technology reinforce each other in a balanced manner&quot;. This framework, however, rarely finds concrete application in schools, especially in our country. Here the teaching of statistics, besides being marginal and incorporated in the mathematical curriculum, is very often dealt in an abstract and formal way. There is very limited emphasis on the meaning of concepts an...

In C. Reading (Ed.), Data and context in statistics education: Towards an evidence-based society. Proceedings of the Eighth International Conference on Teaching Statistics (ICOTS8, July, 2010), Ljubljana, Slovenia. Voorburg, The Netherlands: International Statistical Institute. www.stat.auckland.ac.nz/~iase/publications.php [© 2010 ISI/IASE] THE IMPORTANCE OF TEACHING STATISTICS IN A PROFESSIONAL CONTEXT

Teaching Statistics in a Data Rich World IASE Satellite Conference

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The teaching of statistics has been gaining prominence and affirming itself as an area of study, especially due to the need to qualify the training of professionals. In this paper, we aim to discuss the history of statistics teaching based on two existing reviews of literature written in 1995 and 2007, respectively. Therefore, we conducted our own review, focusing on higher education in the health area. In regards to the studies that have been published since 2007, it is evident an increase in the use of the Internet, the search for methods for teaching specific contents and the effort to promote literacy as well as the distribution of statistical results. We believe that the usage of methodologies based on learning theories and developed through research studies on real data supported by virtual and face-to-face technologies is appropriate with the evolution that the teaching of statistics demands. This link may improve the training of health professionals.

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Sat / act prep online guides and tips, 113 great research paper topics.

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One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

  • Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
  • Analyze the impact a famous artist had on the world.
  • How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
  • How has the music of slaves brought over from Africa shaped modern American music?
  • How has rap music evolved in the past decade?
  • How has the portrayal of minorities in the media changed?

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Current Events

  • What have been the impacts of China's one child policy?
  • How have the goals of feminists changed over the decades?
  • How has the Trump presidency changed international relations?
  • Analyze the history of the relationship between the United States and North Korea.
  • What factors contributed to the current decline in the rate of unemployment?
  • What have been the impacts of states which have increased their minimum wage?
  • How do US immigration laws compare to immigration laws of other countries?
  • How have the US's immigration laws changed in the past few years/decades?
  • How has the Black Lives Matter movement affected discussions and view about racism in the US?
  • What impact has the Affordable Care Act had on healthcare in the US?
  • What factors contributed to the UK deciding to leave the EU (Brexit)?
  • What factors contributed to China becoming an economic power?
  • Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
  • Do students in schools that eliminate grades do better in college and their careers?
  • Do students from wealthier backgrounds score higher on standardized tests?
  • Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
  • Do students who attend charter schools score higher on standardized tests than students in public schools?
  • Do students learn better in same-sex classrooms?
  • How does giving each student access to an iPad or laptop affect their studies?
  • What are the benefits and drawbacks of the Montessori Method ?
  • Do children who attend preschool do better in school later on?
  • What was the impact of the No Child Left Behind act?
  • How does the US education system compare to education systems in other countries?
  • What impact does mandatory physical education classes have on students' health?
  • Which methods are most effective at reducing bullying in schools?
  • Do homeschoolers who attend college do as well as students who attended traditional schools?
  • Does offering tenure increase or decrease quality of teaching?
  • How does college debt affect future life choices of students?
  • Should graduate students be able to form unions?

body_highschoolsc

  • What are different ways to lower gun-related deaths in the US?
  • How and why have divorce rates changed over time?
  • Is affirmative action still necessary in education and/or the workplace?
  • Should physician-assisted suicide be legal?
  • How has stem cell research impacted the medical field?
  • How can human trafficking be reduced in the United States/world?
  • Should people be able to donate organs in exchange for money?
  • Which types of juvenile punishment have proven most effective at preventing future crimes?
  • Has the increase in US airport security made passengers safer?
  • Analyze the immigration policies of certain countries and how they are similar and different from one another.
  • Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
  • Do tariffs increase the number of domestic jobs?
  • Which prison reforms have proven most effective?
  • Should governments be able to censor certain information on the internet?
  • Which methods/programs have been most effective at reducing teen pregnancy?
  • What are the benefits and drawbacks of the Keto diet?
  • How effective are different exercise regimes for losing weight and maintaining weight loss?
  • How do the healthcare plans of various countries differ from each other?
  • What are the most effective ways to treat depression ?
  • What are the pros and cons of genetically modified foods?
  • Which methods are most effective for improving memory?
  • What can be done to lower healthcare costs in the US?
  • What factors contributed to the current opioid crisis?
  • Analyze the history and impact of the HIV/AIDS epidemic .
  • Are low-carbohydrate or low-fat diets more effective for weight loss?
  • How much exercise should the average adult be getting each week?
  • Which methods are most effective to get parents to vaccinate their children?
  • What are the pros and cons of clean needle programs?
  • How does stress affect the body?
  • Discuss the history of the conflict between Israel and the Palestinians.
  • What were the causes and effects of the Salem Witch Trials?
  • Who was responsible for the Iran-Contra situation?
  • How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
  • What events led to the fall of the Roman Empire?
  • What were the impacts of British rule in India ?
  • Was the atomic bombing of Hiroshima and Nagasaki necessary?
  • What were the successes and failures of the women's suffrage movement in the United States?
  • What were the causes of the Civil War?
  • How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
  • Which factors contributed to the colonies winning the American Revolution?
  • What caused Hitler's rise to power?
  • Discuss how a specific invention impacted history.
  • What led to Cleopatra's fall as ruler of Egypt?
  • How has Japan changed and evolved over the centuries?
  • What were the causes of the Rwandan genocide ?

main_lincoln

  • Why did Martin Luther decide to split with the Catholic Church?
  • Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
  • How did the sexual abuse scandal impact how people view the Catholic Church?
  • How has the Catholic church's power changed over the past decades/centuries?
  • What are the causes behind the rise in atheism/ agnosticism in the United States?
  • What were the influences in Siddhartha's life resulted in him becoming the Buddha?
  • How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

  • How has the earth's climate changed in the past few decades?
  • How has the use and elimination of DDT affected bird populations in the US?
  • Analyze how the number and severity of natural disasters have increased in the past few decades.
  • Analyze deforestation rates in a certain area or globally over a period of time.
  • How have past oil spills changed regulations and cleanup methods?
  • How has the Flint water crisis changed water regulation safety?
  • What are the pros and cons of fracking?
  • What impact has the Paris Climate Agreement had so far?
  • What have NASA's biggest successes and failures been?
  • How can we improve access to clean water around the world?
  • Does ecotourism actually have a positive impact on the environment?
  • Should the US rely on nuclear energy more?
  • What can be done to save amphibian species currently at risk of extinction?
  • What impact has climate change had on coral reefs?
  • How are black holes created?
  • Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
  • How will the loss of net neutrality affect internet users?
  • Analyze the history and progress of self-driving vehicles.
  • How has the use of drones changed surveillance and warfare methods?
  • Has social media made people more or less connected?
  • What progress has currently been made with artificial intelligence ?
  • Do smartphones increase or decrease workplace productivity?
  • What are the most effective ways to use technology in the classroom?
  • How is Google search affecting our intelligence?
  • When is the best age for a child to begin owning a smartphone?
  • Has frequent texting reduced teen literacy rates?

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How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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  • Indian J Anaesth
  • v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g001.jpg

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

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Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

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where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

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where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

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where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

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where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

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where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

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Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

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Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

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Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

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If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

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PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

  • The assumption of normality which specifies that the means of the sample group are normally distributed
  • The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

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where X = sample mean, u = population mean and SE = standard error of mean

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where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

  • To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

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where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

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where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

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Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

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A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

  • StatPages.net – provides links to a number of online power calculators
  • G-Power – provides a downloadable power analysis program that runs under DOS
  • Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
  • SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

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1. Introduction

Statistics is a body of quantitative methods associated with empirical observation. A primary goal of these methods is coping with uncertainty. Most formal statistical methods rely on probability theory to express this uncertainty and to provide a formal mathematical basis for data description and for analysis. The notion of variability associated with data, expressed through probability, plays a fundamental role in this theory. As a consequence, much statistical effort is focused on how to control and measure variability and/or how to assign it to its sources.

Almost all characterizations of statistics as a field include the following elements:

(a) Designing experiments, surveys, and other systematic forms of empirical study.

(b) Summarizing and extracting information from data.

(c) Drawing formal inferences from empirical data through the use of probability.

(d) Communicating the results of statistical investigations to others, including scientists, policy makers, and the public.

This research paper describes a number of these elements, and the historical context out of which they grew. It provides a broad overview of the field, that can serve as a starting point to many of the other statistical entries in this encyclopedia.

2. The Origins Of The Field of Statistics

The word ‘statistics’ is related to the word ‘state’ and the original activity that was labeled as statistics was social in nature and related to elements of society through the organization of economic, demographic, and political facts. Paralleling this work to some extent was the development of the probability calculus and the theory of errors, typically associated with the physical sciences. These traditions came together in the nineteenth century and led to the notion of statistics as a collection of methods for the analysis of scientific data and the drawing of inferences therefrom.

As Hacking (1990) has noted: ‘By the end of the century chance had attained the respectability of a Victorian valet, ready to be the logical servant of the natural, biological and social sciences’ ( p. 2). At the beginning of the twentieth century, we see the emergence of statistics as a field under the leadership of Karl Pearson, George Udny Yule, Francis Y. Edgeworth, and others of the ‘English’ statistical school. As Stigler (1986) suggests:

Before 1900 we see many scientists of different fields developing and using techniques we now recognize as belonging to modern statistics. After 1900 we begin to see identifiable statisticians developing such techniques into a unified logic of empirical science that goes far beyond its component parts. There was no sharp moment of birth; but with Pearson and Yule and the growing number of students in Pearson’s laboratory, the infant discipline may be said to have arrived. (p. 361)

Pearson’s laboratory at University College, London quickly became the first statistics department in the world and it was to influence subsequent developments in a profound fashion for the next three decades. Pearson and his colleagues founded the first methodologically-oriented statistics journal, Biometrika, and they stimulated the development of new approaches to statistical methods. What remained before statistics could legitimately take on the mantle of a field of inquiry, separate from mathematics or the use of statistical approaches in other fields, was the development of the formal foundations of theories of inference from observations, rooted in an axiomatic theory of probability.

Beginning at least with the Rev. Thomas Bayes and Pierre Simon Laplace in the eighteenth century, most early efforts at statistical inference used what was known as the method of inverse probability to update a prior probability using the observed data in what we now refer to as Bayes’ Theorem. (For a discussion of who really invented Bayes’ Theorem, see Stigler 1999, Chap. 15). Inverse probability came under challenge in the nineteenth century, but viable alternative approaches gained little currency. It was only with the work of R. A. Fisher on statistical models, estimation, and significance tests, and Jerzy Neyman and Egon Pearson, in the 1920s and 1930s, on tests of hypotheses, that alternative approaches were fully articulated and given a formal foundation. Neyman’s advocacy of the role of probability in the structuring of a frequency-based approach to sample surveys in 1934 and his development of confidence intervals further consolidated this effort at the development of a foundation for inference (cf. Statistical Methods, History of: Post- 1900 and the discussion of ‘The inference experts’ in Gigerenzer et al. 1989).

At about the same time Kolmogorov presented his famous axiomatic treatment of probability, and thus by the end of the 1930s, all of the requisite elements were finally in place for the identification of statistics as a field. Not coincidentally, the first statistical society devoted to the mathematical underpinnings of the field, The Institute of Mathematical Statistics, was created in the United States in the mid-1930s. It was during this same period that departments of statistics and statistical laboratories and groups were first formed in universities in the United States.

3. Emergence Of Statistics As A Field

3.1 the role of world war ii.

Perhaps the greatest catalysts to the emergence of statistics as a field were two major social events: the Great Depression of the 1930s and World War II. In the United States, one of the responses to the depression was the development of large-scale probability-based surveys to measure employment and unemployment. This was followed by the institutionalization of sampling as part of the 1940 US decennial census. But with World War II raging in Europe and in Asia, mathematicians and statisticians were drawn into the war effort, and as a consequence they turned their attention to a broad array of new problems. In particular, multiple statistical groups were established in both England and the US specifically to develop new methods and to provide consulting. (See Wallis 1980, on statistical groups in the US; Barnard and Plackett 1985, for related efforts in the United Kingdom; and Fienberg 1985). These groups not only created imaginative new techniques such as sequential analysis and statistical decision theory, but they also developed a shared research agenda. That agenda led to a blossoming of statistics after the war, and in the 1950s and 1960s to the creation of departments of statistics at universities—from coast to coast in the US, and to a lesser extent in England and elsewhere.

3.2 The Neo-Bayesian Revival

Although inverse probability came under challenge in the 1920s and 1930s, it was not totally abandoned. John Maynard Keynes (1921) wrote A Treatise on Probability that was rooted in this tradition, and Frank Ramsey (1926) provided an early effort at justifying the subjective nature of prior distributions and suggested the importance of utility functions as an adjunct to statistical inference. Bruno de Finetti provided further development of these ideas in the 1930s, while Harold Jeffreys (1938) created a separate ‘objective’ development of these and other statistical ideas on inverse probability.

Yet as statistics flourished in the post-World War II era, it was largely based on the developments of Fisher, Neyman and Pearson, as well as the decision theory methods of Abraham Wald (1950). L. J. Savage revived interest in the inverse probability approach with The Foundations of Statistics (1954) in which he attempted to provide the axiomatic foundation from the subjective perspective. In an essentially independent effort, Raiffa and Schlaifer (1961) attempted to provide inverse probability counterparts to many of the then existing frequentist tools, referring to these alternatives as ‘Bayesian.’ By 1960, the term ‘Bayesian inference’ had become standard usage in the statistical literature, the theoretical interest in the development of Bayesian approaches began to take hold, and the neo-Bayesian revival was underway. But the movement from Bayesian theory to statistical practice was slow, in large part because the computations associated with posterior distributions were an overwhelming stumbling block for those who were interested in the methods. Only in the 1980s and 1990s did new computational approaches revolutionize both Bayesian methods, and the interest in them, in a broad array of areas of application.

3.3 The Role Of Computation In Statistics

From the days of Pearson and Fisher, computation played a crucial role in the development and application of statistics. Pearson’s laboratory employed dozens of women who used mechanical devices to carry out the careful and painstaking calculations required to tabulate values from various probability distributions. This effort ultimately led to the creation of the Biometrika Tables for Statisticians that were so widely used by others applying tools such as chisquare tests and the like. Similarly, Fisher also developed his own set of statistical tables with Frank Yates when he worked at Rothamsted Experiment Station in the 1920s and 1930s. One of the most famous pictures of Fisher shows him seated at Whittingehame Lodge, working at his desk calculator (see Box 1978).

The development of the modern computer revolutionized statistical calculation and practice, beginning with the creation of the first statistical packages in the 1960s—such as the BMDP package for biological and medical applications, and Datatext for statistical work in the social sciences. Other packages soon followed—such as SAS and SPSS for both data management and production-like statistical analyses, and MINITAB for the teaching of statistics. In 2001, in the era of the desktop personal computer, almost everyone has easy access to interactive statistical programs that can implement complex statistical procedures and produce publication-quality graphics. And there is a new generation of statistical tools that rely upon statistical simulation such as the bootstrap and Markov Chain Monte Carlo methods. Complementing the traditional production-like packages for statistical analysis are more methodologically oriented languages such as S and S-PLUS, and symbolic and algebraic calculation packages. Statistical journals and those in various fields of application devote considerable space to descriptions of such tools.

4. Statistics At The End Of The Twentieth Century

It is widely recognized that any statistical analysis can only be as good as the underlying data. Consequently, statisticians take great care in the the design of methods for data collection and in their actual implementation. Some of the most important modes of statistical data collection include censuses, experiments, observational studies, and sample Surveys, all of which are discussed elsewhere in this encyclopedia. Statistical experiments gain their strength and validity both through the random assignment of treatments to units and through the control of nontreatment variables. Similarly sample surveys gain their validity for generalization through the careful design of survey questionnaires and probability methods used for the selection of the sample units. Approaches to cope with the failure to fully implement randomization in experiments or random selection in sample surveys are discussed in Experimental Design: Compliance and Nonsampling Errors.

Data in some statistical studies are collected essentially at a single point in time (cross-sectional studies), while in others they are collected repeatedly at several time points or even continuously, while in yet others observations are collected sequentially, until sufficient information is available for inferential purposes. Different entries discuss these options and their strengths and weaknesses.

After a century of formal development, statistics as a field has developed a number of different approaches that rely on probability theory as a mathematical basis for description, analysis, and statistical inference. We provide an overview of some of these in the remainder of this section and provide some links to other entries in this encyclopedia.

4.1 Data Analysis

The least formal approach to inference is often the first employed. Its name stems from a famous article by John Tukey (1962), but it is rooted in the more traditional forms of descriptive statistical methods used for centuries.

Today, data analysis relies heavily on graphical methods and there are different traditions, such as those associated with

(a) The ‘exploratory data analysis’ methods suggested by Tukey and others.

(b) The more stylized correspondence analysis techniques of Benzecri and the French school.

(c) The alphabet soup of computer-based multivariate methods that have emerged over the past decade such as ACE, MARS, CART, etc.

No matter which ‘school’ of data analysis someone adheres to, the spirit of the methods is typically to encourage the data to ‘speak for themselves.’ While no theory of data analysis has emerged, and perhaps none is to be expected, the flexibility of thought and method embodied in the data analytic ideas have influenced all of the other approaches.

4.2 Frequentism

The name of this group of methods refers to a hypothetical infinite sequence of data sets generated as was the data set in question. Inferences are to be made with respect to this hypothetical infinite sequence. (For details, see Frequentist Inference).

One of the leading frequentist methods is significance testing, formalized initially by R. A. Fisher (1925) and subsequently elaborated upon and extended by Neyman and Pearson and others (see below). Here a null hypothesis is chosen, for example, that the mean, µ, of a normally distributed set of observations is 0. Fisher suggested the choice of a test statistic, e.g., based on the sample mean, x, and the calculation of the likelihood of observing an outcome as or more extreme as x is from µ 0, a quantity usually labeled as the p-value. When p is small (e.g., less than 5 percent), either a rare event has occurred or the null hypothesis is false. Within this theory, no probability can be given for which of these two conclusions is the case.

A related set of methods is testing hypotheses, as proposed by Neyman and Pearson (1928, 1932). In this approach, procedures are sought having the property that, for an infinite sequence of such sets, in only (say) 5 percent for would the null hypothesis be rejected if the null hypothesis were true. Often the infinite sequence is restricted to sets having the same sample size, but this is unnecessary. Here, in addition to the null hypothesis, an alternative hypothesis is specified. This permits the definition of a power curve, reflecting the frequency of rejecting the null hypothesis when the specified alternative is the case. But, as with the Fisherian approach, no probability can be given to either the null or the alternative hypotheses.

The construction of confidence intervals, following the proposal of Neyman (1934), is intimately related to testing hypotheses; indeed a 95 percent confidence interval may be regarded as the set of null hypotheses which, had they been tested at the 5 percent level of significance, would not have been rejected. A confidence interval is a random interval, having the property that the specified proportion (say 95 percent) of the infinite sequence, of random intervals would have covered the true value. For example, an interval that 95 percent of the time (by auxiliary randomization) is the whole real line, and 5 percent of the time is the empty set, is a valid 95 percent confidence interval.

Estimation of parameters—i.e., choosing a single value of the parameters that is in some sense best—is also an important frequentist method. Many methods have been proposed, both for particular models and as general approaches regardless of model, and their frequentist properties explored. These methods usually extended to intervals of values through inversion of test statistics or via other related devices. The resulting confidence intervals share many of the frequentist theoretical properties of the corresponding test procedures.

Frequentist statisticians have explored a number of general properties thought to be desirable in a procedure, such as invariance, unbiasedness, sufficiency, conditioning on ancillary statistics, etc. While each of these properties has examples in which it appears to produce satisfactory recommendations, there are others in which it does not. Additionally, these properties can conflict with each other. No general frequentist theory has emerged that proposes a hierarchy of desirable properties, leaving a frequentist without guidance in facing a new problem.

4.3 Likelihood Methods

The likelihood function (first studied systematically by R. A. Fisher) is the probability density of the data, viewed as a function of the parameters. It occupies an interesting middle ground in the philosophical debate, as it is used both by frequentists (as in maximum likelihood estimation) and by Bayesians in the transition from prior distributions to posterior distributions. A small group of scholars (among them G. A. Barnard, A. W. F. Edwards, R. Royall, D. Sprott) have proposed the likelihood function as an independent basis for inference. The issue of nuisance parameters has perplexed this group, since maximization, as would be consistent with maximum likelihood estimation, leads to different results in general than does integration, which would be consistent with Bayesian ideas.

4.4 Bayesian Methods

Both frequentists and Bayesians accept Bayes’ Theorem as correct, but Bayesians use it far more heavily. Bayesian analysis proceeds from the idea that probability is personal or subjective, reflecting the views of a particular person at a particular point in time. These views are summarized in the prior distribution over the parameter space. Together the prior distribution and the likelihood function define the joint distribution of the parameters and the data. This joint distribution can alternatively be factored as the product of the posterior distribution of the parameter given the data times the predictive distribution of the data.

In the past, Bayesian methods were deemed to be controversial because of the avowedly subjective nature of the prior distribution. But the controversy surrounding their use has lessened as recognition of the subjective nature of the likelihood has spread. Unlike frequentist methods, Bayesian methods are, in principle, free of the paradoxes and counterexamples that make classical statistics so perplexing. The development of hierarchical modeling and Markov Chain Monte Carlo (MCMC) methods have further added to the current popularity of the Bayesian approach, as they allow analyses of models that would otherwise be intractable.

Bayesian decision theory, which interacts closely with Bayesian statistical methods, is a useful way of modeling and addressing decision problems of experimental designs and data analysis and inference. It introduces the notion of utilities and the optimum decision combines probabilities of events with utilities by the calculation of expected utility and maximizing the latter (e.g., see the discussion in Lindley 2000).

Current research is attempting to use the Bayesian approach to hypothesis testing to provide tests and pvalues with good frequentist properties (see Bayarri and Berger 2000).

4.5 Broad Models: Nonparametrics And Semiparametrics

These models include parameter spaces of infinite dimensions, whether addressed in a frequentist or Bayesian manner. In a sense, these models put more inferential weight on the assumption of conditional independence than does an ordinary parametric model.

4.6 Some Cross-Cutting Themes

Often different fields of application of statistics need to address similar issues. For example, dimensionality of the parameter space is often a problem. As more parameters are added, the model will in general fit better (at least no worse). Is the apparent gain in accuracy worth the reduction in parsimony? There are many different ways to address this question in the various applied areas of statistics.

Another common theme, in some sense the obverse of the previous one, is the question of model selection and goodness of fit. In what sense can one say that a set of observations is well-approximated by a particular distribution? (cf. Goodness of Fit: Overview). All statistical theory relies at some level on the use of formal models, and the appropriateness of those models and their detailed specification are of concern to users of statistical methods, no matter which school of statistical inference they choose to work within.

5. Statistics In The Twenty-first Century

5.1 adapting and generalizing methodology.

Statistics as a field provides scientists with the basis for dealing with uncertainty, and, among other things, for generalizing from a sample to a population. There is a parallel sense in which statistics provides a basis for generalization: when similar tools are developed within specific substantive fields, such as experimental design methodology in agriculture and medicine, and sample surveys in economics and sociology. Statisticians have long recognized the common elements of such methodologies and have sought to develop generalized tools and theories to deal with these separate approaches (see e.g., Fienberg and Tanur 1989).

One hallmark of modern statistical science is the development of general frameworks that unify methodology. Thus the tools of Generalized Linear Models draw together methods for linear regression and analysis of various models with normal errors and those log-linear and logistic models for categorical data, in a broader and richer framework. Similarly, graphical models developed in the 1970s and 1980s use concepts of independence to integrate work in covariance section, decomposable log-linear models, and Markov random field models, and produce new methodology as a consequence. And the latent variable approaches from psychometrics and sociology have been tied with simultaneous equation and measurement error models from econometrics into a broader theory of covariance analysis and structural equations models.

Another hallmark of modern statistical science is the borrowing of methods in one field for application in another. One example is provided by Markov Chain Monte Carlo methods, now used widely in Bayesian statistics, which were first used in physics. Survival analysis, used in biostatistics to model the disease-free time or time-to-mortality of medical patients, and analyzed as reliability in quality control studies, are now used in econometrics to measure the time until an unemployed person gets a job. We anticipate that this trend of methodological borrowing will continue across fields of application.

5.2 Where Will New Statistical Developments Be Focused ?

In the issues of its year 2000 volume, the Journal of the American Statistical Association explored both the state of the art of statistics in diverse areas of application, and that of theory and methods, through a series of vignettes or short articles. These essays provide an excellent supplement to the entries of this encyclopedia on a wide range of topics, not only presenting a snapshot of the current state of play in selected areas of the field but also affecting some speculation on the next generation of developments. In an afterword to the last set of these vignettes, Casella (2000) summarizes five overarching themes that he observed in reading through the entire collection:

(a) Large datasets.

(b) High-dimensional/nonparametric models.

(c) Accessible computing.

(d) Bayes/frequentist/who cares?

(e) Theory/applied/why differentiate?

Not surprisingly, these themes fit well those that one can read into the statistical entries in this encyclopedia. The coming together of Bayesian and frequentist methods, for example, is illustrated by the movement of frequentists towards the use of hierarchical models and the regular consideration of frequentist properties of Bayesian procedures (e.g., Bayarri and Berger 2000). Similarly, MCMC methods are being widely used in non-Bayesian settings and, because they focus on long-run sequences of dependent draws from multivariate probability distributions, there are frequentist elements that are brought to bear in the study of the convergence of MCMC procedures. Thus the oft-made distinction between the different schools of statistical inference (suggested in the preceding section) is not always clear in the context of real applications.

5.3 The Growing Importance Of Statistics Across The Social And Behavioral Sciences

Statistics touches on an increasing number of fields of application, in the social sciences as in other areas of scholarship. Historically, the closest links have been with economics; together these fields share parentage of econometrics. There are now vigorous interactions with political science, law, sociology, psychology, anthropology, archeology, history, and many others.

In some fields, the development of statistical methods has not been universally welcomed. Using these methods well and knowledgeably requires an understanding both of the substantive field and of statistical methods. Sometimes this combination of skills has been difficult to develop.

Statistical methods are having increasing success in addressing questions throughout the social and behavioral sciences. Data are being collected and analyzed on an increasing variety of subjects, and the analyses are becoming increasingly sharply focused on the issues of interest.

We do not anticipate, nor would we find desirable, a future in which only statistical evidence was accepted in the social and behavioral sciences. There is room for, and need for, many different approaches. Nonetheless, we expect the excellent progress made in statistical methods in the social and behavioral sciences in recent decades to continue and intensify.

Bibliography:

  • Barnard G A, Plackett R L 1985 Statistics in the United Kingdom, 1939–1945. In: Atkinson A C, Fienberg S E (eds.) A Celebration of Statistics: The ISI Centennial Volume. Springer-Verlag, New York, pp. 31–55
  • Bayarri M J, Berger J O 2000 P values for composite null models (with discussion). Journal of the American Statistical Association 95: 1127–72
  • Box J 1978 R. A. Fisher, The Life of a Scientist. Wiley, New York
  • Casella G 2000 Afterword. Journal of the American Statistical Association 95: 1388
  • Fienberg S E 1985 Statistical developments in World War II: An international perspective. In: Anthony C, Atkinson A C, Fienberg S E (eds.) A Celebration of Statistics: The ISI Centennial Volume. Springer-Verlag, New York, pp. 25–30
  • Fienberg S E, Tanur J M 1989 Combining cognitive and statistical approaches to survey design. Science 243: 1017–22
  • Fisher R A 1925 Statistical Methods for Research Workers. Oliver and Boyd, London
  • Gigerenzer G, Swijtink Z, Porter T, Daston L, Beatty J, Kruger L 1989 The Empire of Chance. Cambridge University Press, Cambridge, UK
  • Hacking I 1990 The Taming of Chance. Cambridge University Press, Cambridge, UK
  • Jeffreys H 1938 Theory of Probability, 2nd edn. Clarendon Press, Oxford, UK
  • Keynes J 1921 A Treatise on Probability. Macmillan, London
  • Lindley D V 2000/1932 The philosophy of statistics (with discussion). The Statistician 49: 293–337
  • Neyman J 1934 On the two different aspects of the representative method: the method of stratified sampling and the method of purposive selection (with discussion). Journal of the Royal Statistical Society 97: 558–625
  • Neyman J, Pearson E S 1928 On the use and interpretation of certain test criteria for purposes of statistical inference. Part I. Biometrika 20A: 175–240
  • Neyman J, Pearson E S 1932 On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society, Series. A 231: 289–337
  • Raiffa H, Schlaifer R 1961 Applied Statistical Decision Theory. Harvard Business School, Boston
  • Ramsey F P 1926 Truth and probability. In: The Foundations of Mathematics and Other Logical Essays. Kegan Paul, London, pp.
  • Savage L J 1954 The Foundations of Statistics. Wiley, New York
  • Stigler S M 1986 The History of Statistics: The Measurement of Uncertainty Before 1900. Harvard University Press, Cambridge, MA
  • Stigler S M 1999 Statistics on the Table: The History of Statistical Concepts and Methods. Harvard University Press, Cambridge, MA
  • Tukey John W 1962 The future of data analysis. Annals of Mathematical Statistics 33: 1–67
  • Wald A 1950 Statistical Decision Functions. Wiley, New York
  • Wallis W 1980 The Statistical Research Group, 1942–1945 (with discussion). Journal of the American Statistical Association 75: 320–35

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Labor Migration, Capital Accumulation, and the Structure of Rural Labor Markets

Between 1967 and 1974, a bilateral treaty increased circular labor migration from Malawi to South Africa by 200%, bringing over 53 million USD in earnings into origin communities. A deadly migrant worker plane crash in 1974 ended these flows and led to migrant repatriation. We study how this shock affected local labor markets. In regions receiving more migrant capital after the crash, workers, particularly women, shifted from farming into non-farm work over thirty years. Investments in non-farm physical and human capital contribute to these sectoral changes. This natural experiment shows that temporary capital inflows can permanently reshape rural labor markets.

This is the authors' final version of the paper, which has been accepted for publication at the Review of Economics and Statistics. UK DFID and the IZA funded this work for the benefit of developing countries (GA-C2-RA4-181). Views expressed are those of the authors and not necessarily those of DFID (now FCDO), IZA, or the National Bureau of Economic Research.

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How To Write a Statistical Research Paper: Tips, Topics, Outline

Statistical Research Paper

Working on a research paper can be a bit challenging. Some people even opt for paying online writing companies to do the job for them. While this might seem like a better solution, it can cost you a lot of money. A cheaper option is to search online for the critical parts of your essay. Your data should come from reliable sources for your research paper to be authentic. You will also need to introduce your work to your readers. It should be straightforward and relevant to the topic.  With this in mind, here is a guideline to help you succeed in your research writing. But before that, let’s see what the outline should look like.

The Outline

Table of Contents

How to write a statistical analysis paper is a puzzle many people find difficult to crack. It’s not such a challenging task as you might think, especially if you learn some helpful tips to make the writing process easier. It’s just like working on any other essay. You only need to get the format and structure right and study the process. Here is what the general outline should look like:

  • introduction;
  • problem statement;
  • objectives;
  • methodology;
  • data examination;
  • discussion;
  • conclusion and recommendations.

Let us now see some tips that can help you become a better statistical researcher.

  • Top 99+ Trending Statistics Research Topics for Students

Tips for Writing Statistics Research Paper

If you are wondering how people write their papers, you are in the right place. We’ll take a look at a few pointers that can help you come up with amazing work.

Choose A Topic

Basically, this is the most important stage of your essay. Whether you want to pay for it or not, you need a simple and accessible topic to write about. Usually, the paid research papers have a well-formed and clear topic. It helps your paper to stand out. Start off by explaining to your audience what your papers are all about. Also, check whether there is enough data to support your idea. The weaker the topic is, the harder your work will be. Is the potential theme within the realm of statistics? Can the question at hand be solved with the help of the available data? These are some of the questions someone should answer first. In the end, the topic you opt for should provide sufficient space for independent information collection and analysis.

Collect Data

This stage relies heavily on the quantity of data sources and the method used to collect them. Keep in mind that you must stick to the chosen methodology throughout your essay. It is also important to explain why you opted for the data collection method used. Plus, be cautious when collecting information. One simple mistake can compromise the entire work. You can source your data from reliable sources like google, read published articles, or experiment with your own findings. However, if your instructor provides certain recommendations, follow them instead. Don’t twist the information to fit your interest to avoid losing originality. And in case no recommendations are given, ask your instructor to provide some.

Write Body Paragraphs

Use the information garnered to create the main body of your essay. After identifying an applicable area of interest, use the data to build your paragraphs. You can start off by making a rough draft of your findings and then use it as a guide for your main essay. The next step is to construe numerical figures and make conclusions. This stage requires your proficiency in interpreting statistics. Integrate your math engagement strategies to break down those figures and pinpoint only the most meaningful parts of them. Also, include some common counterpoints and support the information with specific examples.

Create Your Essay

Now that you have all the appropriate materials at hand, this section will be easy. Simply note down all the information gathered, citing your sources as well. Make sure not to copy and paste directly to avoid plagiarism. Your content should be unique and easy to read, too. We recommend proofreading and polishing your work before making it public. In addition, be on the lookout for any grammatical, spelling, or punctuation mistakes.

This section is a summary of all your findings. Explain the importance of what you are doing. You can also include suggestions for future work. Make sure to restate what you mentioned in the introduction and touch a little bit on the method used to collect and analyze your data. In short, sum up everything you’ve written in your essay.

How to Find Statistical Topics for your Paper

Statistics is a discipline that involves collecting, analyzing, organizing, presenting, and interpreting data. If you are looking for the right topic for your work, here are a few things to consider.

●   Start by finding out what topics have already been worked on and pick the remaining areas.

●   Consider recent developments in your field of study that may inspire a new topic.

●   Think about any specific questions or problems that you have come across on your own that could be explored further.

●   Ask your advisor or mentor for suggestions.

●   Review conference proceedings, journal articles, and other publications.

●   Try using a brainstorming technique. For instance, list out related keywords and combine them in different ways to generate new ideas.

Try out some of these tips. Be sure to find something that will work for you.

Working on a statistics paper can be quite challenging to work on. But with the right information sources, everything becomes easy. This guide will help you reveal the secret of preparing such essays. Also, don’t forget to do more reading to broaden your knowledge. You can find statistics research paper examples and refer to them for ideas. Nonetheless, if you’re still not confident enough, you can always hire a trustworthy writing company to get the job done.

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Why Collaboration Is Critical in Uncertain Times

  • Jenny Fernandez,
  • Kathryn Landis,

research paper topics on statistics

Working together can catalyze innovation — even in risk-averse companies.

Recent research suggests that when resources become limited, many business leaders’ inclinations are to become risk-averse and protect their own interests, fostering a culture of conservatism and prioritizing stability over innovation. In such circumstances, the emphasis often shifts toward preserving existing assets, reducing expenditures, and maintaining the status quo, which can hinder the organization’s ability to adapt, pivot, and thrive in a competitive environment. However, it’s precisely during these challenging times that the untapped potential of collaboration can be a game-changer. If you’re a leader struggling with risk-taking, here are four strategies to make the mindset and behavior shifts to become more collaborative and unlock growth.

A client of ours — let’s call her Mary, a senior executive in the technology industry — faced significant challenges managing a large organization amid economic uncertainty. Both her company and industry were experiencing tough times, resulting in budget cuts and a hiring freeze. Moreover, she was tasked with exceeding her annual revenue goals to compensate for the underperformance of a struggling business line, which was beyond her direct control.

  • Jenny Fernandez , MBA, is an executive and team coach, Columbia and NYU faculty, and future of work and brand strategist. She works with senior leaders and their teams to become more collaborative, innovative, and resilient. Her work spans Fortune 500 companies, startups, and higher education. Jenny has been recognized by LinkedIn as a “Top Voice in Executive Coaching, Leadership Development, and Personal Branding” and was invited to join the prestigious Marshall Goldsmith’s 100 Coaches community. She is a Gen Z advocate. Connect with her on LinkedIn .
  • Kathryn Landis , MBA, is the founder and CEO of the global coaching and advisory firm Kathryn Landis Consulting, which helps senior leaders empower and inspire their teams, create a lasting positive impact, and become the best versions of themselves in work and life. She is an adjunct professor at New York University and a former leader at American Express and Automatic Data Processing. Connect with her on LinkedIn .
  • Julie Lee , PhD, is a clinical psychologist, NYU faculty, and a leading Gen Z employment and mental health strategist. Dr. Lee’s work spans Fortune 500 companies,  startups, and higher education institutions, including Harvard and Brown University. In her consulting work, Dr. Lee helps organizations to motivate and retain Gen Z professionals and coaches executives to lead with purpose and empathy. Connect with her on LinkedIn .

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EU AI Act: first regulation on artificial intelligence

The use of artificial intelligence in the EU will be regulated by the AI Act, the world’s first comprehensive AI law. Find out how it will protect you.

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As part of its digital strategy , the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology. AI can create many benefits , such as better healthcare; safer and cleaner transport; more efficient manufacturing; and cheaper and more sustainable energy.

In April 2021, the European Commission proposed the first EU regulatory framework for AI. It says that AI systems that can be used in different applications are analysed and classified according to the risk they pose to users. The different risk levels will mean more or less regulation. Once approved, these will be the world’s first rules on AI.

Learn more about what artificial intelligence is and how it is used

What Parliament wants in AI legislation

Parliament’s priority is to make sure that AI systems used in the EU are safe, transparent, traceable, non-discriminatory and environmentally friendly. AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes.

Parliament also wants to establish a technology-neutral, uniform definition for AI that could be applied to future AI systems.

Learn more about Parliament’s work on AI and its vision for AI’s future

AI Act: different rules for different risk levels

The new rules establish obligations for providers and users depending on the level of risk from artificial intelligence. While many AI systems pose minimal risk, they need to be assessed.

Unacceptable risk

Unacceptable risk AI systems are systems considered a threat to people and will be banned. They include:

  • Cognitive behavioural manipulation of people or specific vulnerable groups: for example voice-activated toys that encourage dangerous behaviour in children
  • Social scoring: classifying people based on behaviour, socio-economic status or personal characteristics
  • Biometric identification and categorisation of people
  • Real-time and remote biometric identification systems, such as facial recognition

Some exceptions may be allowed for law enforcement purposes. “Real-time” remote biometric identification systems will be allowed in a limited number of serious cases, while “post” remote biometric identification systems, where identification occurs after a significant delay, will be allowed to prosecute serious crimes and only after court approval.

AI systems that negatively affect safety or fundamental rights will be considered high risk and will be divided into two categories:

1) AI systems that are used in products falling under the EU’s product safety legislation . This includes toys, aviation, cars, medical devices and lifts.

2) AI systems falling into specific areas that will have to be registered in an EU database:

  • Management and operation of critical infrastructure
  • Education and vocational training
  • Employment, worker management and access to self-employment
  • Access to and enjoyment of essential private services and public services and benefits
  • Law enforcement
  • Migration, asylum and border control management
  • Assistance in legal interpretation and application of the law.

All high-risk AI systems will be assessed before being put on the market and also throughout their lifecycle.

General purpose and generative AI

Generative AI, like ChatGPT, would have to comply with transparency requirements:

  • Disclosing that the content was generated by AI
  • Designing the model to prevent it from generating illegal content
  • Publishing summaries of copyrighted data used for training

High-impact general-purpose AI models that might pose systemic risk, such as the more advanced AI model GPT-4, would have to undergo thorough evaluations and any serious incidents would have to be reported to the European Commission.

Limited risk

Limited risk AI systems should comply with minimal transparency requirements that would allow users to make informed decisions. After interacting with the applications, the user can then decide whether they want to continue using it. Users should be made aware when they are interacting with AI. This includes AI systems that generate or manipulate image, audio or video content, for example deepfakes.

On December 9 2023, Parliament reached a provisional agreement with the Council on the AI act . The agreed text will now have to be formally adopted by both Parliament and Council to become EU law. Before all MEPs have their say on the agreement, Parliament’s internal market and civil liberties committees will vote on it.

More on the EU’s digital measures

  • Cryptocurrency dangers and the benefits of EU legislation
  • Fighting cybercrime: new EU cybersecurity laws explained
  • Boosting data sharing in the EU: what are the benefits?
  • EU Digital Markets Act and Digital Services Act
  • Five ways the European Parliament wants to protect online gamers
  • Artificial Intelligence Act

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This section features overview and background articles for the general public. Press releases and materials for news media are available in the news section .

Read our research on: Immigration & Migration | Podcasts | Election 2024

Regions & Countries

How americans view the situation at the u.s.-mexico border, its causes and consequences, 80% say the u.s. government is doing a bad job handling the migrant influx.

research paper topics on statistics

Pew Research Center conducted this study to understand the public’s views about the large number of migrants seeking to enter the U.S. at the border with Mexico. For this analysis, we surveyed 5,140 adults from Jan. 16-21, 2024. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for the report and its methodology .

The growing number of migrants seeking entry into the United States at its border with Mexico has strained government resources, divided Congress and emerged as a contentious issue in the 2024 presidential campaign .

Chart shows Why do Americans think there is an influx of migrants to the United States?

Americans overwhelmingly fault the government for how it has handled the migrant situation. Beyond that, however, there are deep differences – over why the migrants are coming to the U.S., proposals for addressing the situation, and even whether it should be described as a “crisis.”

Factors behind the migrant influx

Economic factors – either poor conditions in migrants’ home countries or better economic opportunities in the United States – are widely viewed as major reasons for the migrant influx.

About seven-in-ten Americans (71%), including majorities in both parties, cite better economic opportunities in the U.S. as a major reason.

There are wider partisan differences over other factors.

About two-thirds of Americans (65%) say violence in migrants’ home countries is a major reason for why a large number of immigrants have come to the border.

Democrats and Democratic-leaning independents are 30 percentage points more likely than Republicans and Republican leaners to cite this as a major reason (79% vs. 49%).

By contrast, 76% of Republicans say the belief that U.S. immigration policies will make it easy to stay in the country once they arrive is a major factor. About half as many Democrats (39%) say the same.

For more on Americans’ views of these and other reasons, visit Chapter 2.

How serious is the situation at the border?

A sizable majority of Americans (78%) say the large number of migrants seeking to enter this country at the U.S.-Mexico border is eithera crisis (45%) or a major problem (32%), according to the Pew Research Center survey, conducted Jan. 16-21, 2024, among 5,140 adults.

Related: Migrant encounters at the U.S.-Mexico border hit a record high at the end of 2023 .

Chart shows Border situation viewed as a ‘crisis’ by most Republicans; Democrats are more likely to call it a ‘problem’

  • Republicans are much more likely than Democrats to describe the situation as a “crisis”: 70% of Republicans say this, compared with just 22% of Democrats.
  • Democrats mostly view the situation as a major problem (44%) or minor problem (26%) for the U.S. Very few Democrats (7%) say it is not a problem.

In an open-ended question , respondents voice their concerns about the migrant influx. They point to numerous issues, including worries about how the migrants are cared for and general problems with the immigration system.

Yet two concerns come up most frequently:

  • 22% point to the economic burdens associated with the migrant influx, including the strains migrants place on social services and other government resources.
  • 22% also cite security concerns. Many of these responses focus on crime (10%), terrorism (10%) and drugs (3%).

When asked specifically about the impact of the migrant influx on crime in the United States, a majority of Americans (57%) say the large number of migrants seeking to enter the country leads to more crime. Fewer (39%) say this does not have much of an impact on crime in this country.

Republicans (85%) overwhelmingly say the migrant surge leads to increased crime in the U.S. A far smaller share of Democrats (31%) say the same; 63% of Democrats instead say it does not have much of an impact.

Government widely criticized for its handling of migrant influx

For the past several years, the federal government has gotten low ratings for its handling of the situation at the U.S.-Mexico border. (Note: The wording of this question has been modified modestly to reflect circumstances at the time).

Chart shows Only about a quarter of Democrats and even fewer Republicans say the government has done a good job dealing with large number of migrants at the border

However, the current ratings are extraordinarily low.

Just 18% say the U.S. government is doing a good job dealing with the large number of migrants at the border, while 80% say it is doing a bad job, including 45% who say it is doing a very bad job.

  • Republicans’ views are overwhelmingly negative (89% say it’s doing a bad job), as they have been since Joe Biden became president.
  • 73% of Democrats also give the government negative ratings, the highest share recorded during Biden’s presidency.

For more on Americans’ evaluations of the situation, visit Chapter 1 .

Which policies could improve the border situation?

There is no single policy proposal, among the nine included on the survey, that majorities of both Republicans and Democrats say would improve the situation at the U.S.-Mexico border. There are areas of relative agreement, however.

A 60% majority of Americans say that increasing the number of immigration judges and staff in order to make decisions on asylum more quickly would make the situation better. Only 11% say it would make things worse, while 14% think it would not make much difference.

Nearly as many (56%) say creating more opportunities for people to legally immigrate to the U.S. would make the situation better.

Chart shows Most Democrats and nearly half of Republicans say boosting resources for quicker decisions on asylum cases would improve situation at Mexico border

Majorities of Democrats say each of these proposals would make the border situation better.

Republicans are less positive than are Democrats; still, about 40% or more of Republicans say each would improve the situation, while far fewer say they would make things worse.

Opinions on other proposals are more polarized. For example, a 56% majority of Democrats say that adding resources to provide safe and sanitary conditions for migrants arriving in the U.S. would be a positive step forward.

Republicans not only are far less likely than Democrats to view this proposal positively, but far more say it would make the situation worse (43%) than better (17%).

Chart shows Wide partisan gaps in views of expanding border wall, providing ‘safe and sanitary conditions’ for migrants

Building or expanding a wall along the U.S.-Mexico border was among the most divisive policies of Donald Trump’s presidency. In 2019, 82% of Republicans favored expanding the border wall , compared with just 6% of Democrats.

Today, 72% of Republicans say substantially expanding the wall along the U.S. border with Mexico would make the situation better. Just 15% of Democrats concur, with most saying either it would not make much of a difference (47%) or it would make things worse (24%).

For more on Americans’ reactions to policy proposals, visit Chapter 3 .

Facts are more important than ever

In times of uncertainty, good decisions demand good data. Please support our research with a financial contribution.

Report Materials

Table of contents, fast facts on how greeks see migrants as greece-turkey border crisis deepens, americans’ immigration policy priorities: divisions between – and within – the two parties, from the archives: in ’60s, americans gave thumbs-up to immigration law that changed the nation, around the world, more say immigrants are a strength than a burden, latinos have become less likely to say there are too many immigrants in u.s., most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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