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500+ 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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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.

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).

Feel free to select any topic and try our free AI essay generator to craft your essay.

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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Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

Research topics and ideas about data science and big data analytics

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

Developing machine learning models for real-time fraud detection in online transactions.
The use of big data analytics in predicting and managing urban traffic flow.
Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
The application of predictive analytics in personalizing cancer treatment plans.
Analyzing consumer behavior through big data to enhance retail marketing strategies.
The role of data science in optimizing renewable energy generation from wind farms.
Developing natural language processing algorithms for real-time news aggregation and summarization.
The application of big data in monitoring and predicting epidemic outbreaks.
Investigating the use of machine learning in automating credit scoring for microfinance.
The role of data analytics in improving patient care in telemedicine.
Developing AI-driven models for predictive maintenance in the manufacturing industry.
The use of big data analytics in enhancing cybersecurity threat intelligence.
Investigating the impact of sentiment analysis on brand reputation management.
The application of data science in optimizing logistics and supply chain operations.
Developing deep learning techniques for image recognition in medical diagnostics.
The role of big data in analyzing climate change impacts on agricultural productivity.
Investigating the use of data analytics in optimizing energy consumption in smart buildings.
The application of machine learning in detecting plagiarism in academic works.
Analyzing social media data for trends in political opinion and electoral predictions.
The role of big data in enhancing sports performance analytics.
Developing data-driven strategies for effective water resource management.
The use of big data in improving customer experience in the banking sector.
Investigating the application of data science in fraud detection in insurance claims.
The role of predictive analytics in financial market risk assessment.
Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

The application of big data in public transportation systems for route optimization.
Investigating the impact of big data analytics on e-commerce recommendation systems.
The use of data mining techniques in understanding consumer preferences in the entertainment industry.
Developing predictive models for real estate pricing and market trends.
The role of big data in tracking and managing environmental pollution.
Investigating the use of data analytics in improving airline operational efficiency.
The application of machine learning in optimizing pharmaceutical drug discovery.
Analyzing online customer reviews to inform product development in the tech industry.
The role of data science in crime prediction and prevention strategies.
Developing models for analyzing financial time series data for investment strategies.
The use of big data in assessing the impact of educational policies on student performance.
Investigating the effectiveness of data visualization techniques in business reporting.
The application of data analytics in human resource management and talent acquisition.
Developing algorithms for anomaly detection in network traffic data.
The role of machine learning in enhancing personalized online learning experiences.
Investigating the use of big data in urban planning and smart city development.
The application of predictive analytics in weather forecasting and disaster management.
Analyzing consumer data to drive innovations in the automotive industry.
The role of data science in optimizing content delivery networks for streaming services.
Developing machine learning models for automated text classification in legal documents.
The use of big data in tracking global supply chain disruptions.
Investigating the application of data analytics in personalized nutrition and fitness.
The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
Developing predictive models for customer churn in the telecommunications industry.
The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies, so they can provide some useful insight as to what a research topic looks like in practice.

Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
An Essay on How Data Science Can Strengthen Business (Santos, 2023)
A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest. In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

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If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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155 Best Statistics Project Topics for College Students

Are you a college student seeking an exciting project that blends your love for numbers with real-world impact? Your search ends here! Statistics projects are your gateway to unlock the power of data analysis and make a difference. The first step? Selecting the perfect project topic. It’s the foundation of your success.

In this blog, we’ve made it easy for you. We’ve compiled a list of the best statistics project topics for college students, ensuring you have a wealth of options to choose from. Let’s dive into the world of statistics and find the ideal project that’ll make your academic journey truly remarkable.

Table of Contents

What are Statistics Topics?

Statistics topics encompass a wide range of subjects within the field of data analysis. These topics involve the collection, interpretation, and presentation of numerical data to draw meaningful conclusions. Some common statistics topics include data analysis, hypothesis testing, regression analysis, predictive modeling, and more. These topics are applied in various fields such as finance, healthcare, sports, psychology, and environmental science, to name a few. Statistics project topics for college students help researchers and analysts make informed decisions, solve real-world problems, and uncover patterns and trends within data, making them a fundamental aspect of academic and practical research.

Why Choose the Right Statistics Project Topic?

Before we dive into the list of statistics project topics for college students, you need to know the importance of choosing the project topics of statistics. Choosing the right statistics project topic is of paramount importance for several reasons:

Relevance: A well-chosen topic ensures that your project aligns with your academic and career goals.
Motivation: Selecting a topic that genuinely interests you keeps you motivated throughout the project.
Data Availability: It ensures that there is sufficient data available for analysis, preventing potential roadblocks.
Real-World Impact: A carefully chosen topic can lead to practical applications and contribute to solving real-world problems.
Academic Success: The right topic increases the likelihood of academic success, leading to higher grades and a stronger understanding of statistical concepts.
Career Opportunities: A project aligned with your interests can open doors to career opportunities in your chosen field.
Personal Growth: It allows you to grow as a statistician or data analyst, gaining valuable skills and experience.

Also Read: Best Project Ideas for Software Engineering

List of Statistics Project Topics for College Students

Here is a complete list of statistics project topics for college students in 2023:

Descriptive Statistics

Mean, Median, and Mode Analysis in Different Datasets
Variance and Standard Deviation Comparison in Various Fields
Exploring Measures of Central Tendency in Finance
Analyzing Data Skewness and Kurtosis
Quartile and Percentile Analysis in Health Data
Frequency Distribution of Crime Rates in Different Regions
Interquartile Range Examination in Educational Data
Comparative Study of Dispersion in Sales Data
Histogram Analysis for Population Growth
Time Series Analysis of Temperature Data
Measures of Spread in Sports Statistics
Analysis of Wealth Distribution using Box Plots
Exploring Descriptive Statistics in Environmental Data
Examining Data Distribution in Political Surveys
Analyzing Income Inequality using Gini Coefficient
Correlation and Covariance in Social Sciences

Hypothesis Testing

Testing the Gender Pay Gap Hypothesis
T-Test Analysis of Educational Interventions
Chi-Square Analysis in Healthcare Outcomes
ANOVA Testing in Market Research
Z-Test for Hypothesis in Retail Data
Paired T-Test for Employee Productivity
Wilcoxon Rank-Sum Test in Customer Satisfaction
McNemar’s Test in Social Media Usage
Kruskal-Wallis Test for Regional Sales Comparison
Mann-Whitney U Test in Product Preferences
Two-Proportion Z-Test in Voting Behavior
Poisson Test in Accident Frequency
Testing the Null Hypothesis in Quality Control
Analysis of Correlation Significance in Marriage Age
Hypothesis Testing in Criminal Justice Reform
A/B Testing for Website Conversion Rates

Regression Analysis

Simple Linear Regression in Predicting House Prices
Multiple Regression Analysis in Car Mileage
Logistic Regression for Credit Risk Assessment
Polynomial Regression for Stock Market Prediction
Ridge Regression in Environmental Impact Assessment
Lasso Regression in Movie Box Office Predictions
Time Series Forecasting with Exponential Smoothing
ARIMA Modeling for Sales Forecasting
Regression Trees for Customer Churn Prediction
Analysis of Non-Linear Regression in Health Data
Stepwise Regression for Predicting Academic Success
Poisson Regression in Traffic Accident Analysis
Logistic Regression for Disease Diagnosis
Hierarchical Regression in Employee Satisfaction
Multiple Regression Analysis in Urban Development
Quantile Regression in Income Prediction

Bayesian Statistics

Bayesian Inference in Drug Efficacy Testing
Bayesian Decision Theory in Investment Strategies
Bayesian Updating in Weather Forecasting
Bayesian Networks for Disease Outbreak Prediction
Bayesian Parameter Estimation in Machine Learning
Markov Chain Monte Carlo (MCMC) in Political Polling
Bayesian Classification in Email Spam Filtering
Bayesian Optimization for Hyperparameter Tuning
Bayesian Survival Analysis in Medical Research
Bayesian Econometrics in Economic Forecasting
Bayesian Analysis of Social Network Data
Bayesian Belief Networks in Fraud Detection
Bayesian Time Series Analysis in Financial Markets
Bayesian Inference in Image Recognition
Bayesian Spatial Analysis for Crime Prediction
Bayesian Meta-Analysis in Clinical Trials

Experimental Design

Factorial Design in Manufacturing Process Optimization
Randomized Controlled Trials in Healthcare Interventions
Latin Square Design in Agricultural Experiments
Split-Plot Design for Quality Control
Response Surface Methodology in Product Development
Completely Randomized Design in Education Assessment
Block Design for Agricultural Field Trials
Fractional Factorial Design in Chemical Engineering
Cross-Over Design in Drug Testing
Two-Level Factorial Design for Marketing Campaigns
Nested Design in Wildlife Ecology Studies
Factorial ANOVA in Psychological Experiments
Repeated Measures Design in Sports Performance Analysis
Taguchi Design of Experiments in Engineering
D-Optimal Design in Clinical Trials
Central Composite Design for Food Process Optimization

Nonparametric Statistics

Wilcoxon Signed-Rank Test in Employee Salaries
Mann-Whitney U Test in Online Shopping Habits
Kruskal-Wallis Test for Restaurant Ratings
Spearman’s Rank Correlation in Social Media Metrics
Friedman Test in Voting Preference Analysis
Sign Test in Stock Price Movement
Kendall’s Tau in Customer Satisfaction
Anderson-Darling Test for Data Normality
McNemar’s Test for Medical Diagnosis
Kolmogorov-Smirnov Test in Marketing Analytics
Nonparametric Regression Analysis in Real Estate
The Hodges-Lehmann Estimator in Financial Data
Nonparametric Tests for Time Series Data
Mann-Whitney U Test in Product Reviews
Mood’s Median Test in Consumer Preferences
Comparing Nonparametric Tests in Various Fields

Multivariate Analysis

Principal Component Analysis in Financial Risk Assessment
Factor Analysis for Customer Satisfaction
Canonical Correlation Analysis in Marketing Research
Discriminant Analysis for Species Classification
Cluster Analysis in Social Network Grouping
Multidimensional Scaling for Image Similarity
MANOVA in Psychological Assessment
Redundancy Analysis in Environmental Impact Studies
Structural Equation Modeling (SEM) for Education
Canonical Discriminant Analysis in Healthcare Outcomes
Correspondence Analysis for Political Surveys
Path Analysis in Consumer Behavior
Multiway Analysis in Image Compression
Discriminant Analysis in Credit Scoring
Cluster Analysis for Customer Segmentation
Multivariate Time Series Analysis in Stock Prices

Survival Analysis

Kaplan-Meier Survival Analysis in Cancer Studies
Cox Proportional Hazards Model in Finance
Log-Rank Test in Epidemiology
Weibull Distribution in Engineering Reliability
Parametric Survival Models in Pharmaceutical Trials
Survival Analysis in Employee Retention
Competing Risk Survival Analysis in Healthcare
Bayesian Survival Analysis in Disease Progression
Nonparametric Survival Analysis in Social Sciences
Survival Analysis in Customer Churn
Survival Analysis for Product Durability
Time-Dependent Covariates in Survival Studies
Frailty Models in Aging Research
Cure Models in Medical Research
Event History Analysis in Demography
Survival Analysis of Wildlife Populations

Time Series Analysis

Autocorrelation Function (ACF) and Partial ACF (PACF) Analysis
Box-Jenkins Methodology for ARIMA Modeling
Seasonal Decomposition of Time Series (STL)
Exponential Smoothing Methods for Forecasting
GARCH Models for Financial Volatility
State Space Models for Economic Time Series
Time Series Clustering Techniques
Granger Causality Testing in Macroeconomics
ARMA-GARCH Models in Stock Market Volatility
Time Series Forecasting in Energy Consumption
Wavelet Transform Analysis in Signal Processing
Multivariate Time Series Forecasting in Supply Chain
Long Short-Term Memory (LSTM) in Deep Learning
Time Series Decomposition in Retail Sales
Vector Autoregression (VAR) Models in Macroeconomic Analysis
Time Series Analysis in Weather Forecasting

Machine Learning and Big Data

Predictive Analytics using Machine Learning Algorithms
Feature Selection Techniques in Big Data Analysis
Random Forest Classification in Customer Churn Prediction
Support Vector Machines (SVM) for Anomaly Detection
Natural Language Processing (NLP) for Sentiment Analysis
Clustering and Association Analysis in Market Basket Data
Recommender Systems in E-commerce
Deep Learning for Image Recognition
Time Series Forecasting with Recurrent Neural Networks (RNN)
Text Mining and Topic Modeling for Social Media Data
Ensemble Learning Methods in Credit Scoring
Big Data Analysis using Hadoop and Spark
Classification and Regression Trees (CART) in Healthcare
Unsupervised Learning for Customer Segmentation
Machine Learning in Fraud Detection
Dimensionality Reduction Techniques in High-Dimensional Data

These statistics project topics for college students should provide a diverse range of options for their statistics projects across various fields and methodologies.

How to Select the Perfect Statistics Project Topic?

Selecting the perfect statistics project topics for college students involves the following steps:

Identify Your Interests: Choose a topic that genuinely interests you as it will keep you motivated throughout the project.
Research Existing Data: Ensure that data related to your chosen topic is accessible and can be used for analysis.
Define a Clear Objective: Clearly state the purpose of your project and the questions you aim to answer.
Consult with Professors: Seek guidance from your professors to ensure the feasibility and relevance of your chosen topic.
Consider Real-world Impact: Think about how your project can contribute to solving real-world problems or advancing a particular field.
Plan Your Methodology: Outline the statistical techniques and tools you intend to use for analysis.
Stay Organized: Keep detailed records of your work, data sources, and results to make the reporting phase easier.

In conclusion, the significance of selecting the right statistics project topics for college students cannot be overstated. It is the initial stride on your academic journey that sets the stage for a fulfilling and impactful experience. Fortunately, the diverse array of statistics project topics, spanning fields like sports, healthcare, finance, and psychology, ensures that there’s something for everyone. Your project is not merely an academic exercise but a chance to explore your passion and contribute meaningfully to your chosen area of study. By adhering to the steps outlined for topic selection, you can confidently venture into the world of statistics, where learning and discovery go hand in hand. So, choose wisely and embark on a statistical journey that promises both knowledge and fulfillment.

FAQs (Statistics Project Topics for College Students)

1. can i choose a statistics project topic outside my major.

Absolutely! Choosing a topic that interests you is more important than sticking to your major.

2. How do I access the necessary data for my project?

You can find datasets online, in academic libraries, or by collaborating with professionals in relevant fields.

Innovative Statistics Project Ideas for Insightful Analysis

Table of contents

1.1 AP Statistics Topics for Project
1.2 Statistics Project Topics for High School Students
1.3 Statistical Survey Topics
1.4 Statistical Experiment Ideas
1.5 Easy Stats Project Ideas
1.6 Business Ideas for Statistics Project
1.7 Socio-Economic Easy Statistics Project Ideas
1.8 Experiment Ideas for Statistics and Analysis
2 Conclusion: Navigating the World of Data Through Statistics

Diving into the world of data, statistics presents a unique blend of challenges and opportunities to uncover patterns, test hypotheses, and make informed decisions. It is a fascinating field that offers many opportunities for exploration and discovery. This article is designed to inspire students, educators, and statistics enthusiasts with various project ideas. We will cover:

Challenging concepts suitable for advanced placement courses.
Accessible ideas that are engaging and educational for younger students.
Ideas for conducting surveys and analyzing the results.
Topics that explore the application of statistics in business and socio-economic areas.

Each category of topics for the statistics project provides unique insights into the world of statistics, offering opportunities for learning and application. Let’s dive into these ideas and explore the exciting world of statistical analysis.

Top Statistics Project Ideas for High School

Statistics is not only about numbers and data; it’s a unique lens for interpreting the world. Ideal for students, educators, or anyone with a curiosity about statistical analysis, these project ideas offer an interactive, hands-on approach to learning. These projects range from fundamental concepts suitable for beginners to more intricate studies for advanced learners. They are designed to ignite interest in statistics by demonstrating its real-world applications, making it accessible and enjoyable for people of all skill levels.

Need help with statistics project? Get your paper written by a professional writer Get Help Reviews.io 4.9/5

AP Statistics Topics for Project

Analyzing Variance in Climate Data Over Decades.
The Correlation Between Economic Indicators and Standard of Living.
Statistical Analysis of Voter Behavior Patterns.
Probability Models in Sports: Predicting Outcomes.
The Effectiveness of Different Teaching Methods: A Statistical Study.
Analysis of Demographic Data in Public Health.
Time Series Analysis of Stock Market Trends.
Investigating the Impact of Social Media on Academic Performance.
Survival Analysis in Clinical Trial Data.
Regression Analysis on Housing Prices and Market Factors.

Statistics Project Topics for High School Students

The Mathematics of Personal Finance: Budgeting and Spending Habits.
Analysis of Class Performance: Test Scores and Study Habits.
A Statistical Comparison of Local Public Transportation Options.
Survey on Dietary Habits and Physical Health Among Teenagers.
Analyzing the Popularity of Various Music Genres in School.
The Impact of Sleep on Academic Performance: A Statistical Approach.
Statistical Study on the Use of Technology in Education.
Comparing Athletic Performance Across Different Sports.
Trends in Social Media Usage Among High School Students.
The Effect of Part-Time Jobs on Student Academic Achievement.

Statistical Survey Topics

Public Opinion on Environmental Conservation Efforts.
Consumer Preferences in the Fast Food Industry.
Attitudes Towards Online Learning vs. Traditional Classroom Learning.
Survey on Workplace Satisfaction and Productivity.
Public Health: Attitudes Towards Vaccination.
Trends in Mobile Phone Usage and Preferences.
Community Response to Local Government Policies.
Consumer Behavior in Online vs. Offline Shopping.
Perceptions of Public Safety and Law Enforcement.
Social Media Influence on Political Opinions.

Statistical Experiment Ideas

The Effect of Light on Plant Growth.
Memory Retention: Visual vs. Auditory Information.
Caffeine Consumption and Cognitive Performance.
The Impact of Exercise on Stress Levels.
Testing the Efficacy of Natural vs. Chemical Fertilizers.
The Influence of Color on Mood and Perception.
Sleep Patterns: Analyzing Factors Affecting Sleep Quality.
The Effectiveness of Different Types of Water Filters.
Analyzing the Impact of Room Temperature on Concentration.
Testing the Strength of Different Brands of Batteries.

Easy Stats Project Ideas

Average Daily Screen Time Among Students.
Analyzing the Most Common Birth Months.
Favorite School Subjects Among Peers.
Average Time Spent on Homework Weekly.
Frequency of Public Transport Usage.
Comparison of Pet Ownership in the Community.
Favorite Types of Movies or TV Shows.
Daily Water Consumption Habits.
Common Breakfast Choices and Their Nutritional Value.
Steps Count: A Week-Long Study.

Business Ideas for Statistics Project

Analyzing Customer Satisfaction in Retail Stores.
Market Analysis of a New Product Launch.
Employee Performance Metrics and Organizational Success.
Sales Data Analysis for E-commerce Websites.
Impact of Advertising on Consumer Buying Behavior.
Analysis of Supply Chain Efficiency.
Customer Loyalty and Retention Strategies.
Trend Analysis in Social Media Marketing.
Financial Risk Assessment in Investment Decisions.
Market Segmentation and Targeting Strategies.

Socio-Economic Easy Statistics Project Ideas

Income Inequality and Its Impact on Education.
The Correlation Between Unemployment Rates and Crime Levels.
Analyzing the Effects of Minimum Wage Changes.
The Relationship Between Public Health Expenditure and Population Health.
Demographic Analysis of Housing Affordability.
The Impact of Immigration on Local Economies.
Analysis of Gender Pay Gap in Different Industries.
Statistical Study of Homelessness Causes and Solutions.
Education Levels and Their Impact on Job Opportunities.
Analyzing Trends in Government Social Spending.

Experiment Ideas for Statistics and Analysis

Multivariate Analysis of Global Climate Change Data.
Time-Series Analysis in Predicting Economic Recessions.
Logistic Regression in Medical Outcome Prediction.
Machine Learning Applications in Statistical Modeling.
Network Analysis in Social Media Data.
Bayesian Analysis of Scientific Research Data.
The Use of Factor Analysis in Psychology Studies.
Spatial Data Analysis in Geographic Information Systems (GIS).
Predictive Analysis in Customer Relationship Management (CRM).
Cluster Analysis in Market Research.

Conclusion: Navigating the World of Data Through Statistics

In this exploration of good statistics project ideas, we’ve ventured through various topics, from the straightforward to the complex, from personal finance to global climate change. These ideas are gateways to understanding the world of data and statistics, and platforms for cultivating critical thinking and analytical skills. Whether you’re a high school student, a college student, or a professional, engaging in these projects can deepen your appreciation of how statistics shapes our understanding of the world around us. These projects encourage exploration, inquiry, and a deeper engagement with the world of numbers, trends, and patterns – the essence of statistics.

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The Importance of a Good Statistics Topic

Why would you need our statistics project topics list? What makes a good statistics topic so important? The truth is that professors are subjective when it comes to essays and topics. Most of them will award bonus points to students who manage to come up with interesting statistics project topic ideas. After all, a great topic means you’ve invested a lot of time and effort into the paper, studied popular and scholarly sources to write it. We know that original statistics project topics are hard to come by, so we’ve created a list of 100 brand new topics for 2023.

Statistics Projects Topics

Our ENL writers compiled a list of the most common statistics projects topics. You can easily write an essay on these in one or two days because they don’t require much research:

Using statistics in actuarial science
Analyze an example of statistical signal processing
Compare the Smith chart and the Sankey diagram
Discuss the correlation coefficient
Practical application of the Metropolis-Hastings algorithm
Getting ready for a world of robots

Easy Statistics Research Topics

We have a list of easy statistics research topics that you can surely handle all by yourself. Choose one of these topics and start writing:

Using statistics in epidemiology
Applications of statistical physics
Pros and cons of the Stemplot and Radar chart
Using a Venn diagram correctly
Child marriages in Africa (statistics)
Discuss the analysis of variance (ANOVA) process
Discuss the Box–Jenkins method

Statistical Research Topic for High School

Are you a high school student who needs to find a great statistics idea for an essay? Check out the following statistical research topic for high school:

Using statistics in chemometrics
Statistics and business analytics
Discuss the field of statistical thermodynamics
Principal component analysis in multivariate statistics
What is a kernel density estimation?
Selecting the correct sample for a survey
What are cross-sectional studies?

Most Interesting Topics in Statistics

We’ve included all of the most interesting topics in statistics in a separate list. You can find the best of the best right here:

Using statistics in machine learning
What are statistical finance processes?
Statistics in quality control in 2023
Compare and contrast the Skewplot and the Sparkline
Using Renkonen similarity index in botanic studies
Calculate the probability of success using the binomial proportion confidence interval
Statistics as a mathematical science

Hot Topics for Statistics Projects

Some ideas are better than others, especially when it comes to finding a good topic. Here are what we consider to be very hot topics for statistics projects:

Using statistics in jurimetrics
What are environmental statistics?
Compare the curve fitting and smoothing processes
Analyze 3 GEEs (Generalized estimating equations)
Discuss the Rule of three in medicine
The Goodman and Kruskal’s lambda measure

Survey Topics for Statistics

Conducting a survey is not that difficult, we agree. However, finding a good topic for your survey is. Pick one of our survey topics for statistics and start organizing the survey in minutes:

Gather information about the GPA from 70 students in your university
Survey how much time students spend doing their homework
Make a survey on surveys
Make a survey about the English language in high school
What is your favorite city survey
What do you think about our government survey
Are you satisfied with your life survey

Good Topics for Statistics Projects

This is the list where you can find the topics that are not breathtaking. Check out these good topics for statistics projects and select one today:

Analyze the Markov Chain central limit theorem
Discuss the loop-erased random walk model
Bernoulli matrix vs the Centering matrix in statistics
Using statistics in psychometrics
Interpreting the total sum of squares correctly
Apply Kuder–Richardson’s Formula 20 in psychometrics

AP Statistics Topics

Advanced Placement Statistics is one of the most difficult courses for college students. This is why we want to help you with some very interesting AP statistics topics:

Getting an adjacency matrix quickly
What is the orthostochastic matrix?
Obtaining the transition matrix optimally
Discuss econometrics and its role
Analyze the pros of the Probit Model
Categorical data analysis and the Cochran–Armitage test for trend
The history of probability

Theoretical Statistics Topics for a Core Course

If you are looking for some nice theoretical statistics topics for a core course, you have arrived at the right place. Here are some of our best ideas:

Advantages of the Ornstein–Uhlenbeck process
Discuss the Malliavin stochastic calculus
Discuss stochastic optimal control
Discuss homoscedasticity and heteroscedasticity
Predicting errors using the Akaike information criterion
The history of statistics

Business Statistics Topics

Would you like to write about business? Our experienced team of writers and editors managed to come up with these original business statistics topics:

The importance of statistics to business in 2023
Kinds of data in business statistics
Measures of central tendency and dispersion
Discuss inferential statistics
The process of sampling business data
Effective uses of statistics in key business decisions
The effects of probability on business decisions

Good Statistics Projects Topics

We know you want to keep things fresh and get some bonus points for an interesting topic. Here are some very good statistics projects topics that should work great in 2023:

Statistics and the medical treatment of drug addiction
How did Nate Silver predict the outcome of the 2008 US election?
Describe the information theory in statistics
How does AI use the Fuzzy associative matrix?
Composing a questionnaire the right way
Effects of questions on interviewees
The importance of the order of questions in a survey

Statistical Research Topics for College Students

Of course, we have plenty of statistical research topics for college students. These are more difficult than those for high school students, but they should be manageable:

Analyze John Tukey’s contribution to statistics
Florence Nightingale and visual representation in statistics
Discuss Gertrude Cox’s experimental design in statistics
How does statistics improve ADHD treatment?
The Krichevsky–Trofimov estimator in information theory
The timeline of probability in statistics
Discuss Pseudorandomness and Quasirandomness

Controversial Topics for Statistics Project

Just like any field, statistics has its fair share of controversial topics. We managed to gather the most intriguing controversial topics for statistics project right here:

Should we pursue the artificial neural network?
Using the Attack Rate statistic during an epidemic
Discuss the ”admissible decision” rule
The link between statistics and biometrics
Should we abandon null hypothesis significance testing?
Is the Bayes theorem incorrect?

Statistics Research Paper Topics for Graduates

We have a list of statistics research paper topics for graduates, of course. You can get some very nice ideas from these examples:

Discuss Bayesian hierarchical models
Discuss basic AJD (basic affine jump diffusion)
A thorough analysis of Lévy’s continuity theorem
Analyze the Chinese restaurant process
The Cochran–Mantel–Haenszel test
A practical analysis of the principle of maximum entropy
An in-depth look at the Hewitt–Savage Zero–One law

Difficult Statistical Research Topics

If you want to try your hand at a more difficult topic, we can help. Take a quick look at these difficult statistical research topics and choose the one you like:

Statistics and the science of probability
Organizing neurobiological time series data
Analyzing intrinsic fluctuations in biochemical systems
Effective data mining of neurophysiological biomarkers
Econometrics and statistics
Discuss the axioms of probability (Kolmogorov)

Do you think these statistical project topics are not enough to get you a top grade? If you want an awesome statistics project topic, don’t hesitate to contact us. We will think of some unique topics and send them your way right away. Also, we can do much more than just create statistical projects topics. If you need assignment help , editing or proofreading assistance, we are the company to call. We have extensive experience writing essays and term papers for students of all ages. Our PhD writers are ready to spring into action and make sure you turn in an awesome essay – on time!

Get on top of your homework.

Statistic Project Ideas & Topics

How to Select Good Topics for Statistics Projects

Problem solving using data.

Why and how the specific topic was selected
How the research was carried out
What, if any, conclusions were made
The data collected and its analysis
The strengths and the weaknesses of every statistical method used

What is Statistics?

Understanding what is statistics project format.

Learn more: How to master a research paper format ?

Table of Contents
Introduction
Methodologies
Analysis and Results
Problems or Challenges

Have a Look at Statistics Project Examples

Researching reports written by others in your same field either online or in a trade magazine
Asking your teacher to provide you will examples of papers written in the past
Using the schools intranet to look up high quality statistical projects that might have been written by students from previous years
Asking the local or school librarian to help you to find past projects or sources from the library (they could also likely point you in the direction of some really great research material and introduce you to data collection methods that you might not have though of on your own).

Some Sources of Interesting Statistics Project Ideas

https://simplystatistics.org/posts/2012-02-29-statistics-project-ideas-for-students/
https://projectchampionz.com.ng/statistics-project-topics/
https://highered.mheducation.com/sites/0072946814/student_view0/chapter3/project_examples.html

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

General Education

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?

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?

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 ?

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?

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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145 Best Statistics Project Ideas and Topics To Consider

Table of Contents

Are you a statistics student hunting for the best research project ideas? If yes, then this blog post is for you. Basically, statistics is a wide academic discipline that studies data and numerical information in almost all fields and various real-life situations that are related to mathematical science. Since the subject is broad, it might be extremely challenging to brainstorm and identify a good statistics research project topic. Therefore, to make your topic selection process easier, here, we have compiled a list of top statistics project ideas for you to consider. Explore them all and pick any idea that meets your requirements.

List of Statistics Project Ideas

For writing an exclusive statistics research paper, you can choose any of the below-mentioned statistics project ideas that justify your knowledge and understanding.

Statistics Project Ideas for School and College Students

Increasing use of plastics
Statistical analysis of the road accidents in your local area
Course cost differentiation in colleges
Are college students likely to develop drug addictions if given a chance?
Are online classes helpful?
Time spent by college students on social media
Significance of the front seats in the class on success rates
The effects of sitting in the backseat in a class
Should mobile phones be allowed in high school or not?
The ratio of college students getting married after graduation
Relation between birth order as well as success in academics
Is being headstrong difficult, or does it make things easy?
Popular movie genre among students in college
Web browsing habits of college students
Caffeine consumption among students as well as its effect on the performance
Common subjects were chosen by students in college
What types of music do college students like the most?
Are e-books better than conventional books?
Choosing aspects of a subject in college
Do extra-curricular activities help transform personalities?
Should stereotypical social issues be highlighted or not?
Comparison between male as well as female students in college
Comparative study on the pricing of different clothing store prices in your town.
Does the race of actors affect the popularity of TV shows among college students?
Does the experience of a freshman in college with their roommate affect their overall experience at the institution?
Does the effect of a Teacher who is fresher in University influence the student’s performance?
Influence of Distinct Subjects on Student’s Performance.
Significance of Analytics in Studying Statistics.
Influence of Better Student in Class
Influence of Backbenchers in their Performance in Class.
Significance of Medication in Class Performance

Business Statistics Project Ideas

Accessibility of businesses to bank benefits
The influence of social media on business sales
The effect of social media on the performance of an employee
Is consumption of alcohol higher among employees with a lower pay scale?
The impact of cost estimation on business management
Sexual harassment amongst female employees in the workplace
Factors contributing to low productivity in a workplace
Consideration of occupational schedules provided by secretaries
Trends of death management in business entities
Relationship between the leaders of a company as well as its employees
The utilization of modern tools in any organization
The effect of modern communication on the management of a company
The importance of internal communication in a workplace
Can business tools improve the performance of employees?
The significance of assessment analysis.
Correlation of business venture investment in the United States.
Importance of online Business Performance for the impaired workers.
Impact of the Facebook Marketing on Business Sales.
Influence of Supervisors on the CEO of Business.

Statistics Project Ideas on Socio-Economics

Significance of agricultural loans for farmers
Comparison between criminal offenses in town as well as villages
The effect of poverty on crime rates
Food habits in low-income families
Malpractices of low-income groups
Income versus explanation analysis in a society
Peak traffic times in your city
Analysis of road accidents in the suburb as well as the town area
The effect of smoking on medical costs
Analysis of the source of revenue as well as the pattern of expenditure by the local government of the local government
A relationship between exercises as well as a reduction in overall medical expenses
The impact of per capita income on healthcare cost
The reason behind drastic development in any city
Comparison and relation between petroleum prices with food prices
Why is it important to train the youth of low-income families?

Statistics Project Topics on Finance and Economics

Can the growth of an organization in society make a difference in the economy of that community?
Performance analysis of the banking sector
Factors affecting financial distress in the banking sector
Do federal elections affect stock prices?
Do debt reduction policies of the government also reduce the quality of life?
Analysis of cash deposit patterns in banks.
Are computerized budget analysis systems effective?
Statistical analysis of the impact of birth and death rates on the economy of a country
Statistical analysis of infant mortality rate.
Is there a relationship between exercise and a reduction in overall medical costs?
The effects of poor infrastructural facilities on socio-economic development.
Are members of certain subpopulations more likely to get the death penalty?
Analysis of the use of financial reports in assessing the performance of banks.
Analysis of the sources of revenue and the pattern of expenditure of the local government.
Regression analysis on national income.
Income vs Consumption Explanation Study in the Society.
Influence of Advertisement on Health Costs
Study of the Worldwide economic growth.
Influence of Pandemic on Health in the UK

Statistical Analysis Topics on Sports and Movies

Is there a relation between a basketball player as well as his height?
Do sports affect the behavior of an individual?
Do students get lower grades if they are involved in college sports?
Comparison between hockey as well as basketball?
Types of shoes worn by basketball players
Are energy drinks harmful?
The popularity of baseball as well as football
Is the involvement of students in sports the reason behind lower grades?
Statistical analysis of the types of brands endorsed by celebrity sportsmen.
The revolution of cinema
Why is there a demand for movie stars?
Does the cast of a movie influence the interest of people?
Do people enjoy movies more while eating?
Do people enjoy movies more when they eat popcorn?
What are the aspects of a successful movie?
What are the qualities of a great movie?
Do People enjoy commercials vs Art Movies?
Do People in the East like Cricket more than People like Football in the West?
Is Sports or Movies More Enjoyable?

Interesting Statistics Project Topics

Examine the connection between mental health outcomes and social media usage.
Examine the factors that influence voter participation in a particular election or region.
Determine whether a particular marketing promotion strategy is working.
Examine a specific species’ response to climate change.
Analyze the connection between mental health outcomes and exercise.
Examine the connection between a particular area’s health outcomes and income.
Determine whether a particular program or intervention is successful in addressing a particular social problem.
Examine the impact that COVID-19 has had on a particular sector of the economy or industry.
Research the connection between a region’s economic conditions and crime rates.
Investigate the factors that influence a student’s success in a specific academic program.

Statistics Project Ideas for College Assignments

Determine whether there is a correlation between having a part-time job and grades.
Analyze whether there is a correlation between student debt and grades.
Examine the GPAs of students who are from out of state to those who are inside the state.
Determine whether there is a correlation between religious affiliation and grades.
Analyze the academic performance of students who have completed an internship.
Compare the test scores for students who have to work to support themselves vs. those who do not have to work.
Analyze the GPAs of students who are married to those who are not married.
Compare the efficiency of different study methods.
Compare the test scores of African American students to Caucasian students.
Investigate the correlation between intelligence and grades.

Final Words

Out of the wide list of statistics project ideas suggested in this blog post, you can pick the topic of your choice. During the topic selection, keep in mind that the topic you select should have enough data to organize, analyze and interpret. Never pick a project idea that falls out of your interest. In case the topic you have selected is vague, then you can’t write a good hypothesis, and also it may result in low grades. So, spend some time and choose the best statistic project idea that is thought-provoking.

In case, you have no idea what topic to choose for your statistic project or if you need expert help to complete your statistics research project, contact us quickly. On our platform, we have numerous statistics assignments help experts to do your project according to the requirements you share with us. Most importantly, by utilizing our statistics and Strategic Management Assignment Help service, you can also finish your tasks ahead of the deadline and achieve an A+ grade.

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I am an Academic Writer and have affection to share my knowledge through posts’. I do not feel tiredness while research and analyzing the things. Sometime, I write down hundred of research topics as per the students requirements. I want to share solution oriented content to the students.

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Introduction to Statistics

(15 reviews)

David Lane, Rice University

Publisher: David Lane

Language: English

Formats Available

Conditions of use.

Learn more about reviews.

Reviewed by Terri Torres, professor, Oregon Institute of Technology on 8/17/23

This author covers all the topics that would be covered in an introductory statistics course plus some. I could imagine using it for two courses at my university, which is on the quarter system. I would rather have the problem of too many topics... read more

Comprehensiveness rating: 5 see less

Content Accuracy rating: 5

Yes, Lane is both thorough and accurate.

Relevance/Longevity rating: 5

What is covered is what is usually covered in an introductory statistics book. The only topic I may, given sufficient time, cover is bootstrapping.

Clarity rating: 5

The book is clear and well-written. For the trickier topics, simulations are included to help with understanding.

Consistency rating: 5

All is organized in a way that is consistent with the previous topic.

Modularity rating: 5

The text is organized in a way that easily enables navigation.

Organization/Structure/Flow rating: 5

The text is organized like most statistics texts.

Interface rating: 5

Easy navigation.

Grammatical Errors rating: 5

I didn't see any grammatical errors.

Cultural Relevance rating: 5

Nothing is included that is culturally insensitive.

The videos that accompany this text are short and easy to watch and understand. Videos should be short enough to teach, but not so long that they are tiresome. This text includes almost everything: videos, simulations, case studies---all nicely organized in one spot. In addition, Lane has promised to send an instructor's manual and slide deck.

Reviewed by Professor Sandberg, Professor, Framingham State University on 6/29/21

This text covers all the usual topics in an Introduction to Statistics for college students. In addition, it has some additional topics that are useful. read more

This text covers all the usual topics in an Introduction to Statistics for college students. In addition, it has some additional topics that are useful.

I did not find any errors.

Some of the examples are dated. And the frequent use of male/female examples need updating in terms of current gender splits.

I found it was easy to read and understand and I expect that students would also find the writing clear and the explanations accessible.

Even with different authors of chapter, the writing is consistent.

The text is well organized into sections making it easy to assign individual topics and sections.

The topics are presented in the usual order. Regression comes later in the text but there is a difference of opinions about whether to present it early with descriptive statistics for bivariate data or later with inferential statistics.

I had no problem navigating the text online.

The writing is grammatical correct.

I saw no issues that would be offensive.

I did like this text. It seems like it would be a good choice for most introductory statistics courses. I liked that the Monty Hall problem was included in the probability section. The author offers to provide an instructor's manual, PowerPoint slides and additional questions. These additional resources are very helpful and not always available with online OER texts.

Reviewed by Emilio Vazquez, Associate Professor, Trine University on 4/23/21

This appears to be an excellent textbook for an Introductory Course in Statistics. It covers subjects in enough depth to fulfill the needs of a beginner in Statistics work yet is not so complex as to be overwhelming. read more

I found no errors in their discussions. Did not work out all of the questions and answers but my sampling did not reveal any errors.

Some of the examples may need updating depending on the times but the examples are still relevant at this time.

This is a Statistics text so a little dry. I found that the derivation of some of the formulas was not explained. However the background is there to allow the instructor to derive these in class if desired.

The text is consistent throughout using the same verbiage in various sections.

The text dose lend itself to reasonable reading assignments. For example the chapter (Chapter 3) on Summarizing Distributions covers Central Tendency and its associated components in an easy 20 pages with Measures of Variability making up most of the rest of the chapter and covering approximately another 20 pages. Exercises are available at the end of each chapter making it easy for the instructor to assign reading and exercises to be discussed in class.

The textbook flows easily from Descriptive to Inferential Statistics with chapters on Sampling and Estimation preceding chapters on hypothesis testing

I had no problems with navigation

All textbooks have a few errors but certainly nothing glaring or making text difficult

I saw no issues and I am part of a cultural minority in the US

Overall I found this to be a excellent in-depth overview of Statistical Theory, Concepts and Analysis. The length of the textbook appears to be more than adequate for a one-semester course in Introduction to Statistics. As I no longer teach a full statistics course but simply a few lectures as part of our Research Curriculum, I am recommending this book to my students as a good reference. Especially as it is available on-line and in Open Access.

Reviewed by Audrey Hickert, Assistant Professor, Southern Illinois University Carbondale on 3/29/21

All of the major topics of an introductory level statistics course for social science are covered. Background areas include levels of measurement and research design basics. Descriptive statistics include all major measures of central tendency and dispersion/variation. Building blocks for inferential statistics include sampling distributions, the standard normal curve (z scores), and hypothesis testing sections. Inferential statistics include how to calculate confidence intervals, as well as conduct tests of one-sample tests of the population mean (Z- and t-tests), two-sample tests of the difference in population means (Z- and t-tests), chi square test of independence, correlation, and regression. Doesn’t include full probability distribution tables (e.g., t or Z), but those can be easily found online in many places.

I did not find any errors or issues of inaccuracy. When a particular method or practice is debated in the field, the authors acknowledge it (and provide citations in some circumstances).

Relevance/Longevity rating: 4

Basic statistics are standard, so the core information will remain relevant in perpetuity. Some of the examples are dated (e.g., salaries from 1999), but not problematic.

Clarity rating: 4

All of the key terms, formulas, and logic for statistical tests are clearly explained. The book sometimes uses different notation than other entry-level books. For example, the variance formula uses "M" for mean, rather than x-bar.

The explanations are consistent and build from and relate to corresponding sections that are listed in each unit.

Modularity is a strength of this text in both the PDF and interactive online format. Students can easily navigate to the necessary sections and each starts with a “Prerequisites” list of other sections in the book for those who need the additional background material. Instructors could easily compile concise sub-sections of the book for readings.

The presentation of topics differs somewhat from the standard introductory social science statistics textbooks I have used before. However, the modularity allows the instructor and student to work through the discrete sections in the desired order.

Interface rating: 4

For the most part the display of all images/charts is good and navigation is straightforward. One concern is that the organization of the Table of Contents does not exactly match the organizational outline at the start of each chapter in the PDF version. For example, sometimes there are more detailed sub-headings at the start of chapter and occasionally slightly different section headings/titles. There are also inconsistencies in section listings at start of chapters vs. start of sub-sections.

The text is easy to read and free from any obvious grammatical errors.

Although some of the examples are outdated, I did not review any that were offensive. One example of an outdated reference is using descriptive data on “Men per 100 Women” in U.S. cities as “useful if we are looking for an opposite-sex partner”.

This is a good introduction level statistics text book if you have a course with students who may be intimated by longer texts with more detailed information. Just the core basics are provided here and it is easy to select the sections you need. It is a good text if you plan to supplement with an array of your own materials (lectures, practice, etc.) that are specifically tailored to your discipline (e.g., criminal justice and criminology). Be advised that some formulas use different notation than other standard texts, so you will need to point that out to students if they differ from your lectures or assessment materials.

Reviewed by Shahar Boneh, Professor, Metropolitan State University of Denver on 3/26/21, updated 4/22/21

The textbook is indeed quite comprehensive. It can accommodate any style of introductory statistics course. read more

The textbook is indeed quite comprehensive. It can accommodate any style of introductory statistics course.

The text seems to be statistically accurate.

It is a little too extensive, which requires instructors to cover it selectively, and has a potential to confuse the students.

It is written clearly.

Consistency rating: 4

The terminology is fairly consistent. There is room for some improvement.

By the nature of the subject, the topics have to be presented in a sequential and coherent order. However, the book breaks things down quite effectively.

Organization/Structure/Flow rating: 3

Some of the topics are interleaved and not presented in the order I would like to cover them.

Good interface.

The grammar is ok.

The book seems to be culturally neutral, and not offensive in any way.

I really liked the simulations that go with the book. Parts of the book are a little too advanced for students who are learning statistics for the first time.

Reviewed by Julie Gray, Adjunct Assistant Professor, University of Texas at Arlington on 2/26/21

The textbook is for beginner-level students. The concept development is appropriate--there is always room to grow to high higher level, but for an introduction, the basics are what is needed. This is a well-thought-through OER textbook project by... read more

I found all the material accurate.

Essentially, statistical concepts at the introductory level are accepted as universal. This suggests that the relevance of this textbook will continue for a long time.

The book is well written for introducing beginners to statistical concepts. The figures, tables, and animated examples reinforce the clarity of the written text.

Yes, the information is consistent; when it is introduced in early chapters it ties in well in later chapters that build on and add more understanding for the topic.

Modularity rating: 4

The book is well-written with attention to modularity where possible. Due to the nature of statistics, that is not always possible. The content is presented in the order that I usually teach these concepts.

The organization of the book is good, I particularly like the sample lecture slide presentations and the problem set with solutions for use in quizzes and exams. These are available by writing to the author. It is wonderful to have access to these helpful resources for instructors to use in preparation.

I did not find any interface issues.

The book is well written. In my reading I did not notice grammatical errors.

For this subject and in the examples given, I did not notice any cultural issues.

For the field of social work where qualitative data is as common as quantitative, the importance of giving students the rationale or the motivation to learn the quantitative side is understated. To use this text as an introductory statistics OER textbook in a social work curriculum, the instructor will want to bring in field-relevant examples to engage and motivate students. The field needs data-driven decision making and evidence-based practices to become more ubiquitous than not. Preparing future social workers by teaching introductory statistics is essential to meet that goal.

Reviewed by Mamata Marme, Assistant Professor, Augustana College on 6/25/19

This textbook offers a fairly comprehensive summary of what should be discussed in an introductory course in Statistics. The statistical literacy exercises are particularly interesting. It would be helpful to have the statistical tables... read more

Comprehensiveness rating: 4 see less

The terminology and notation used in the textbook is pretty standard. The content is accurate.

The statistical literacy example are up to date but will need to be updated fairly regularly to keep the textbook fresh. The applications within the chapter are accessible and can be used fairly easily over a couple of editions.

The textbook does not necessarily explain the derivation of some of the formulae and this will need to be augmented by the instructor in class discussion. What is beneficial is that there are multiple ways that a topic is discussed using graphs, calculations and explanations of the results. Statistics textbooks have to cover a wide variety of topics with a fair amount of depth. To do this concisely is difficult. There is a fine line between being concise and clear, which this textbook does well, and being somewhat dry. It may be up to the instructor to bring case studies into the readings we are going through the topics rather than wait until the end of the chapter.

The textbook uses standard notation and terminology. The heading section of each chapter is closely tied to topics that are covered. The end of chapter problems and the statistical literacy applications are closely tied to the material covered.

The authors have done a good job treating each chapter as if they stand alone. The lack of connection to a past reference may create a sense of disconnect between the topics discussed

The text's "modularity" does make the flow of the material a little disconnected. If would be better if there was accountability of what a student should already have learnt in a different section. The earlier material is easy to find but not consistently referred to in the text.

I had no problem with the interface. The online version is more visually interesting than the pdf version.

I did not see any grammatical errors.

Cultural Relevance rating: 4

I am not sure how to evaluate this. The examples are mostly based on the American experience and the data alluded to mostly domestic. However, I am not sure if that creates a problem in understanding the methodology.

Overall, this textbook will cover most of the topics in a survey of statistics course.

Reviewed by Alexandra Verkhovtseva, Professor, Anoka-Ramsey Community College on 6/3/19

This is a comprehensive enough text, considering that it is not easy to create a comprehensive statistics textbook. It is suitable for an introductory statistics course for non-math majors. It contains twenty-one chapters, covering the wide range... read more

The content is pretty accurate, I did not find any biases or errors.

The book contains fairly recent data presented in the form of exercises, examples and applications. The topics are up-to-date, and appropriate technology is used for examples, applications, and case studies.

The language is simple and clear, which is a good thing, since students are usually scared of this class, and instructors are looking for something to put them at ease. I would, however, try to make it a little more interesting, exciting, or may be even funny.

Consistency is good, the book has a great structure. I like how each chapter has prerequisites and learner outcomes, this gives students a good idea of what to expect. Material in this book is covered in good detail.

The text can be easily divided into sub-sections, some of which can be omitted if needed. The chapter on regression is covered towards the end (chapter 14), but part of it can be covered sooner in the course.

The book contains well organized chapters that makes reading through easy and understandable. The order of chapters and sections is clear and logical.

The online version has many functions and is easy to navigate. This book also comes with a PDF version. There is no distortion of images or charts. The text is clean and clear, the examples provided contain appropriate format of data presentation.

No grammatical errors found.

The text uses simple and clear language, which is helpful for non-native speakers. I would include more culturally-relevant examples and case studies. Overall, good text.

In all, this book is a good learning experience. It contains tools and techniques that free and easy to use and also easy to modify for both, students and instructors. I very much appreciate this opportunity to use this textbook at no cost for our students.

Reviewed by Dabrina Dutcher, Assistant Professor, Bucknell University on 3/4/19

This is a reasonably thorough first-semester statistics book for most classes. It would have worked well for the general statistics courses I have taught in the past but is not as suitable for specialized introductory statistics courses for engineers or business applications. That is OK, they have separate texts for that! The only sections that feel somewhat light in terms of content are the confidence intervals and ANOVA sections. Given that these topics are often sort of crammed in at the end of many introductory classes, that might not be problematic for many instructors. It should also be pointed out that while there are a couple of chapters on probability, this book spends presents most formulas as "black boxes" rather than worry about the derivation or origin of the formulas. The probability sections do not include any significant combinatorics work, which is sometimes included at this level.

I did not find any errors in the formulas presented but I did not work many end-of-chapter problems to gauge the accuracy of their answers.

There isn't much changing in the introductory stats world, so I have no concerns about the book becoming outdated rapidly. The examples and problems still feel relevant and reasonably modern. My only concern is that the statistical tool most often referenced in the book are TI-83/84 type calculators. As students increasingly buy TI-89s or Inspires, these sections of the book may lose relevance faster than other parts.

Solid. The book gives a list of key terms and their definitions at the end of each chapter which is a nice feature. It also has a formula review at the end of each chapter. I can imagine that these are heavily used by students when studying! Formulas are easy to find and read and are well defined. There are a few areas that I might have found frustrating as a student. For example, the explanation for the difference in formulas for a population vs sample standard deviation is quite weak. Again, this is a book that focuses on sort of a "black-box" approach but you may have to supplement such sections for some students.

I did not detect any problems with inconsistent symbol use or switches in terminology.

Modularity rating: 3

This low rating should not be taken as an indicator of an issue with this book but would be true of virtually any statistics book. Different books still use different variable symbols even for basic calculated statistics. So trying to use a chapter of this book without some sort of symbol/variable cheat-sheet would likely be frustrating to the students.

However, I think it would be possible to skip some chapters or use the chapters in a different order without any loss of functionality.

This book uses a very standard order for the material. The chapter on regressions comes later than it does in some texts but it doesn't really matter since that chapter never seems to fit smoothly anywhere.

There are numerous end of chapter problems, some with answers, available in this book. I'm vacillating on whether these problems would be more useful if they were distributed after each relevant section or are better clumped at the end of the whole chapter. That might be a matter of individual preference.

I did not detect any problems.

I found no errors. However, there were several sections where the punctuation seemed non-ideal. This did not affect the over-all useability of the book though

I'm not sure how well this book would work internationally as many of the examples contain domestic (American) references. However, I did not see anything offensive or biased in the book.

Reviewed by Ilgin Sager, Assistant Professor, University of Missouri - St. Louis on 1/14/19

As the title implies, this is a brief introduction textbook. It covers the fundamental of the introductory statistics, however not a comprehensive text on the subject. A teacher can use this book as the sole text of an introductory statistics. The prose format of definitions and theorems make theoretical concepts accessible to non-math major students. The textbook covers all chapters required in this level course.

It is accurate; the subject matter in the examples to be up to date, is timeless and wouldn't need to be revised in future editions; there is no error except a few typographical errors. There are no logic errors or incorrect explanations.

This text will remain up to date for a long time since it has timeless examples and exercises, it wouldn't be outdated. The information is presented clearly with a simple way and the exercises are beneficial to follow the information.

The material is presented in a clear, concise manner. The text is easy readable for the first time statistics student.

The structure of the text is very consistent. Topics are presented with examples, followed by exercises. Problem sets are appropriate for the level of learner.

When the earlier matters need to be referenced, it is easy to find; no trouble reading the book and finding results, it has a consistent scheme. This book is set very well in sections.

The text presents the information in a logical order.

The learner can easily follow up the material; there is no interface problem.

There is no logic errors and incorrect explanations, a few typographical errors is just to be ignored.

Not applicable for this textbook.

Reviewed by Suhwon Lee, Associate Teaching Professor, University of Missouri on 6/19/18

This book is pretty comprehensive for being a brief introductory book. This book covers all necessary content areas for an introduction to Statistics course for non-math majors. The text book provides an effective index, plenty of exercises, review questions, and practice tests. It provides references and case studies. The glossary and index section is very helpful for students and can be used as a great resource.

Content appears to be accurate throughout. Being an introductory book, the book is unbiased and straight to the point. The terminology is standard.

The content in textbook is up to date. It will be very easy to update it or make changes at any point in time because of the well-structured contents in the textbook.

The author does a great job of explaining nearly every new term or concept. The book is easy to follow, clear and concise. The graphics are good to follow. The language in the book is easily understandable. I found most instructions in the book to be very detailed and clear for students to follow.

Overall consistency is good. It is consistent in terms of terminology and framework. The writing is straightforward and standardized throughout the text and it makes reading easier.

The authors do a great job of partitioning the text and labeling sections with appropriate headings. The table of contents is well organized and easily divisible into reading sections and it can be assigned at different points within the course.

Organization/Structure/Flow rating: 4

Overall, the topics are arranged in an order that follows natural progression in a statistics course with some exception. They are addressed logically and given adequate coverage.

The text is free of any issues. There are no navigation problems nor any display issues.

The text contains no grammatical errors.

The text is not culturally insensitive or offensive in any way most of time. Some examples might need to consider citing the sources or use differently to reflect current inclusive teaching strategies.

Overall, it's well-written and good recourse to be an introduction to statistical methods. Some materials may not need to be covered in an one-semester course. Various examples and quizzes can be a great recourse for instructor.

Reviewed by Jenna Kowalski, Mathematics Instructor, Anoka-Ramsey Community College on 3/27/18

The text includes the introductory statistics topics covered in a college-level semester course. An effective index and glossary are included, with functional hyperlinks. read more

The text includes the introductory statistics topics covered in a college-level semester course. An effective index and glossary are included, with functional hyperlinks.

Content Accuracy rating: 3

The content of this text is accurate and error-free, based on a random sampling of various pages throughout the text. Several examples included information without formal citation, leading the reader to potential bias and discrimination. These examples should be corrected to reflect current values of inclusive teaching.

The text contains relevant information that is current and will not become outdated in the near future. The statistical formulas and calculations have been used for centuries. The examples are direct applications of the formulas and accurately assess the conceptual knowledge of the reader.

The text is very clear and direct with the language used. The jargon does require a basic mathematical and/or statistical foundation to interpret, but this foundational requirement should be met with course prerequisites and placement testing. Graphs, tables, and visual displays are clearly labeled.

The terminology and framework of the text is consistent. The hyperlinks are working effectively, and the glossary is valuable. Each chapter contains modules that begin with prerequisite information and upcoming learning objectives for mastery.

The modules are clearly defined and can be used in conjunction with other modules, or individually to exemplify a choice topic. With the prerequisite information stated, the reader understands what prior mathematical understanding is required to successfully use the module.

The topics are presented well, but I recommend placing Sampling Distributions, Advanced Graphs, and Research Design ahead of Probability in the text. I think this rearranged version of the index would better align with current Introductory Statistics texts. The structure is very organized with the prerequisite information stated and upcoming learner outcomes highlighted. Each module is well-defined.

Adding an option of returning to the previous page would be of great value to the reader. While progressing through the text systematically, this is not an issue, but when the reader chooses to skip modules and read select pages then returning to the previous state of information is not easily accessible.

No grammatical errors were found while reviewing select pages of this text at random.

Cultural Relevance rating: 3

Several examples contained data that were not formally cited. These examples need to be corrected to reflect current inclusive teaching strategies. For example, one question stated that “while men are XX times more likely to commit murder than women, …” This data should be cited, otherwise the information can be interpreted as biased and offensive.

An included solutions manual for the exercises would be valuable to educators who choose to use this text.

Reviewed by Zaki Kuruppalil, Associate Professor, Ohio University on 2/1/18

This is a comprehensive book on statistical methods, its settings and most importantly the interpretation of the results. With the advent of computers and software’s, complex statistical analysis can be done very easily. But the challenge is the knowledge of how to set the case, setting parameters (for example confidence intervals) and knowing its implication on the interpretation of the results. If not done properly this could lead to deceptive inferences, inadvertently or purposely. This book does a great job in explaining the above using many examples and real world case studies. If you are looking for a book to learn and apply statistical methods, this is a great one. I think the author could consider revising the title of the book to reflect the above, as it is more than just an introduction to statistics, may be include the word such as practical guide.

The contents of the book seems accurate. Some plots and calculations were randomly selected and checked for accuracy.

The book topics are up to date and in my opinion, will not be obsolete in the near future. I think the smartest thing the author has done is, not tied the book with any particular software such as minitab or spss . No matter what the software is, standard deviation is calculated the same way as it is always. The only noticeable exception in this case was using the Java Applet for calculating Z values in page 261 and in page 416 an excerpt of SPSS analysis is provided for ANOVA calculations.

The contents and examples cited are clear and explained in simple language. Data analysis and presentation of the results including mathematical calculations, graphical explanation using charts, tables, figures etc are presented with clarity.

Terminology is consistant. Framework for each chapter seems consistent with each chapter beginning with a set of defined topics, and each of the topic divided into modules with each module having a set of learning objectives and prerequisite chapters.

The text book is divided into chapters with each chapter further divided into modules. Each of the modules have detailed learning objectives and prerequisite required. So you can extract a portion of the book and use it as a standalone to teach certain topics or as a learning guide to apply a relevant topic.

Presentation of the topics are well thought and are presented in a logical fashion as if it would be introduced to someone who is learning the contents. However, there are some issues with table of contents and page numbers, for example chapter 17 starts in page 597 not 598. Also some tables and figures does not have a number, for instance the graph shown in page 114 does not have a number. Also it would have been better if the chapter number was included in table and figure identification, for example Figure 4-5 . Also in some cases, for instance page 109, the figures and titles are in two different pages.

No major issues. Only suggestion would be, since each chapter has several modules, any means such as a header to trace back where you are currently, would certainly help.

Grammatical Errors rating: 4

Easy to read and phrased correctly in most cases. Minor grammatical errors such as missing prepositions etc. In some cases the author seems to have the habbit of using a period after the decimal. For instance page 464, 467 etc. For X = 1, Y' = (0.425)(1) + 0.785 = 1.21. For X = 2, Y' = (0.425)(2) + 0.785 = 1.64.

However it contains some statements (even though given as examples) that could be perceived as subjective, which the author could consider citing the sources. For example from page 11: Statistics include numerical facts and figures. For instance: • The largest earthquake measured 9.2 on the Richter scale. • Men are at least 10 times more likely than women to commit murder. • One in every 8 South Africans is HIV positive. • By the year 2020, there will be 15 people aged 65 and over for every new baby born.

Solutions for the exercises would be a great teaching resource to have

Reviewed by Randy Vander Wal, Professor, The Pennsylvania State University on 2/1/18

As a text for an introductory course, standard topics are covered. It was nice to see some topics such as power, sampling, research design and distribution free methods covered, as these are often omitted in abbreviated texts. Each module introduces the topic, has appropriate graphics, illustration or worked example(s) as appropriate and concluding with many exercises. An instructor’s manual is available by contacting the author. A comprehensive glossary provides definitions for all the major terms and concepts. The case studies give examples of practical applications of statistical analyses. Many of the case studies contain the actual raw data. To note is that the on-line e-book provides several calculators for the essential distributions and tests. These are provided in lieu of printed tables which are not included in the pdf. (Such tables are readily available on the web.)

The content is accurate and error free. Notation is standard and terminology is used accurately, as are the videos and verbal explanations therein. Online links work properly as do all the calculators. The text appears neutral and unbiased in subject and content.

The text achieves contemporary relevance by ending each section with a Statistical Literacy example, drawn from contemporary headlines and issues. Of course, the core topics are time proven. There is no obvious material that may become “dated”.

The text is very readable. While the pdf text may appear “sparse” by absence varied colored and inset boxes, pictures etc., the essential illustrations and descriptions are provided. Meanwhile for this same content the on-line version appears streamlined, uncluttered, enhancing the value of the active links. Moreover, the videos provide nice short segments of “active” instruction that are clear and concise. Despite being a mathematical text, the text is not overly burdened by formulas and numbers but rather has “readable feel”.

This terminology and symbol use are consistent throughout the text and with common use in the field. The pdf text and online version are also consistent by content, but with the online e-book offering much greater functionality.

The chapters and topics may be used in a selective manner. Certain chapters have no pre-requisite chapter and in all cases, those required are listed at the beginning of each module. It would be straightforward to select portions of the text and reorganize as needed. The online version is highly modular offering students both ease of navigation and selection of topics.

Chapter topics are arranged appropriately. In an introductory statistics course, there is a logical flow given the buildup to the normal distribution, concept of sampling distributions, confidence intervals, hypothesis testing, regression and additional parametric and non-parametric tests. The normal distribution is central to an introductory course. Necessary precursor topics are covered in this text, while its use in significance and hypothesis testing follow, and thereafter more advanced topics, including multi-factor ANOVA.

Each chapter is structured with several modules, each beginning with pre-requisite chapter(s), learning objectives and concluding with Statistical Literacy sections providing a self-check question addressing the core concept, along with answer, followed by an extensive problem set. The clear and concise learning objectives will be of benefit to students and the course instructor. No solutions or answer key is provided to students. An instructor’s manual is available by request.

The on-line interface works well. In fact, I was pleasantly surprised by its options and functionality. The pdf appears somewhat sparse by comparison to publisher texts, lacking pictures, colored boxes, etc. But the on-line version has many active links providing definitions and graphic illustrations for key terms and topics. This can really facilitate learning as making such “refreshers” integral to the new material. Most sections also have short videos that are professionally done, with narration and smooth graphics. In this way, the text is interactive and flexible, offering varied tools for students. To note is that the interactive e-book works for both IOS and OS X.

The text in pdf form appeared to free of grammatical errors, as did the on-line version, text, graphics and videos.

This text contains no culturally insensitive or offensive content. The focus of the text is on concepts and explanation.

The text would be a great resource for students. The full content would be ambitious for a 1-semester course, such use would be unlikely. The text is clearly geared towards students with no statistics background nor calculus. The text could be used in two styles of course. For 1st year students early chapters on graphs and distributions would be the starting point, omitting later chapters on Chi-square, transformations, distribution-free and size effect chapters. Alternatively, for upper level students the introductory chapters could be bypassed with the latter chapters then covered to completion.

This text adopts a descriptive style of presentation with topics well and fully explained, much like the “Dummy series”. For this, it may seem a bit “wordy”, but this can well serve students and notably it complements powerpoint slides that are generally sparse on written content. This text could be used as the primary text, for regular lectures, or as reference for a “flipped” class. The e-book videos are an enabling tool if this approach is adopted.

Reviewed by David jabon, Associate Professor, DePaul University on 8/15/17

This text covers all the standard topics in a semester long introductory course in statistics. It is particularly well indexed and very easy to navigate. There is comprehensive hyperlinked glossary. read more

This text covers all the standard topics in a semester long introductory course in statistics. It is particularly well indexed and very easy to navigate. There is comprehensive hyperlinked glossary.

The material is completely accurate. There are no errors. The terminology is standard with one exception: the book calls what most people call the interquartile range, the H-spread in a number of places. Ideally, the term "interquartile range" would be used in place of every reference to "H-spread." "Interquartile range" is simply a better, more descriptive term of the concept that it describes. It is also more commonly used nowadays.

This book came out a number of years ago, but the material is still up to date. Some more recent case studies have been added.

The writing is very clear. There are also videos for almost every section. The section on boxplots uses a lot of technical terms that I don't find are very helpful for my students (hinge, H-spread, upper adjacent value).

The text is internally consistent with one exception that I noted (the use of the synonymous words "H-spread" and "interquartile range").

The text book is brokenly into very short sections, almost to a fault. Each section is at most two pages long. However at the end of each of these sections there are a few multiple choice questions to test yourself. These questions are a very appealing feature of the text.

The organization, in particular the ordering of the topics, is rather standard with a few exceptions. Boxplots are introduced in Chapter II before the discussion of measures of center and dispersion. Most books introduce them as part of discussion of summaries of data using measure of center and dispersion. Some statistics instructors may not like the way the text lumps all of the sampling distributions in a single chapter (sampling distribution of mean, sampling distribution for the difference of means, sampling distribution of a proportion, sampling distribution of r). I have tried this approach, and I now like this approach. But it is a very challenging chapter for students.

The book's interface has no features that distracted me. Overall the text is very clean and spare, with no additional distracting visual elements.

The book contains no grammatical errors.

The book's cultural relevance comes out in the case studies. As of this writing there are 33 such case studies, and they cover a wide range of issues from health to racial, ethnic, and gender disparity.

Each chapter as a nice set of exercises with selected answers. The thirty three case studies are excellent and can be supplement with some other online case studies. An instructor's manual and PowerPoint slides can be obtained by emailing the author. There are direct links to online simulations within the text. This text is very high quality textbook in every way.

1. Introduction
2. Graphing Distributions
3. Summarizing Distributions
4. Describing Bivariate Data
5. Probability
6. Research Design
7. Normal Distributions
8. Advanced Graphs
9. Sampling Distributions
10. Estimation
11. Logic of Hypothesis Testing
12. Testing Means
14. Regression
15. Analysis of Variance
16. Transformations
17. Chi Square
18. Distribution-Free Tests
19. Effect Size
20. Case Studies
21. Glossary

Ancillary Material

Ancillary materials are available by contacting the author or publisher .

About the Book

Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available.

About the Contributors

David Lane is an Associate Professor in the Departments of Psychology, Statistics, and Management at the Rice University. Lane is the principal developer of this resource although many others have made substantial contributions. This site was developed at Rice University, University of Houston-Clear Lake, and Tufts University.

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Statistics and probability

Unit 1: analyzing categorical data, unit 2: displaying and comparing quantitative data, unit 3: summarizing quantitative data, unit 4: modeling data distributions, unit 5: exploring bivariate numerical data, unit 6: study design, unit 7: probability, unit 8: counting, permutations, and combinations, unit 9: random variables, unit 10: sampling distributions, unit 11: confidence intervals, unit 12: significance tests (hypothesis testing), unit 13: two-sample inference for the difference between groups, unit 14: inference for categorical data (chi-square tests), unit 15: advanced regression (inference and transforming), unit 16: analysis of variance (anova).

Statistical Research Questions: Five Examples for Quantitative Analysis

Table of contents, introduction.

How are statistical research questions for quantitative analysis written? This article provides five examples of statistical research questions that will allow statistical analysis to take place.

In quantitative research projects, writing statistical research questions requires a good understanding and the ability to discern the type of data that you will analyze. This knowledge is elemental in framing research questions that shall guide you in identifying the appropriate statistical test to use in your research.

Thus, before writing your statistical research questions and reading the examples in this article, read first the article that enumerates the four types of measurement scales . Knowing the four types of measurement scales will enable you to appreciate the formulation or structuring of research questions.

Once you feel confident that you can correctly identify the nature of your data, the following examples of statistical research questions will strengthen your understanding. Asking these questions can help you unravel unexpected outcomes or discoveries particularly while doing exploratory data analysis .

Five Examples of Statistical Research Questions

In writing the statistical research questions, I provide a topic that shows the variables of the study, the study description, and a link to the original scientific article to give you a glimpse of the real-world examples.

Topic 1: Physical Fitness and Academic Achievement

A study was conducted to determine the relationship between physical fitness and academic achievement. The subjects of the study include school children in urban schools.

Statistical Research Question No. 1

Is there a significant relationship between physical fitness and academic achievement?

Notice that this study correlated two variables, namely 1) physical fitness, and 2) academic achievement.

To allow statistical analysis to take place, there is a need to define what is physical fitness, as well as academic achievement. The researchers measured physical fitness in terms of the number of physical fitness tests that the students passed during their physical education class. It’s simply counting the ‘number of PE tests passed.’

On the other hand, the researchers measured academic achievement in terms of a passing score in Mathematics and English. The variable is the number of passing scores in both Mathematics and English.

Both variables are ratio variables.

Given the statistical research question, the appropriate statistical test can be applied to determine the relationship. A Pearson correlation coefficient test will test the significance and degree of the relationship. But the more sophisticated higher level statistical test can be applied if there is a need to correlate with other variables.

In the particular study mentioned, the researchers used multivariate logistic regression analyses to assess the probability of passing the tests, controlling for students’ weight status, ethnicity, gender, grade, and socioeconomic status. For the novice researcher, this requires further study of multivariate (or many variables) statistical tests. You may study it on your own.

Most of what I discuss in the statistics articles I wrote came from self-study. It’s easier to understand concepts now as there are a lot of resource materials available online. Videos and ebooks from places like Youtube, Veoh, The Internet Archives, among others, provide free educational materials. Online education will be the norm of the future. I describe this situation in my post about Education 4.0 .

The following video sheds light on the frequently used statistical tests and their selection. It is an excellent resource for beginners. Just maintain an open mind to get rid of your dislike for numbers; that is, if you are one of those who have a hard time understanding mathematical concepts. My ebook on statistical tests and their selection provides many examples.

Source: Chomitz et al. (2009)

Topic 2: Climate Conditions and Consumption of Bottled Water

This study attempted to correlate climate conditions with the decision of people in Ecuador to consume bottled water, including the volume consumed. Specifically, the researchers investigated if the increase in average ambient temperature affects the consumption of bottled water.

Statistical Research Question No. 2

Is there a significant relationship between average temperature and amount of bottled water consumed?

In this instance, the variables measured include the average temperature in the areas studied and the volume of water consumed . Temperature is an interval variable, while volume is a ratio variable .

In this example, the variables include the average temperature and volume of bottled water . The first variable (average temperature) is an interval variable, and the latter (volume of water) is a ratio variable.

Now, it’s easy to identify the statistical test to analyze the relationship between the two variables. You may refer to my previous post titled Parametric Statistics: Four Widely Used Parametric Tests and When to Use Them . Using the figure supplied in that article, the appropriate test to use is, again, Pearson’s Correlation Coefficient.

Source: Zapata (2021)

Topic 3: Nursing Home Staff Size and Number of COVID-19 Cases

An investigation sought to determine if the size of nursing home staff and the number of COVID-19 cases are correlated. Specifically, they looked into the number of unique employees working daily, and the outcomes include weekly counts of confirmed COVID-19 cases among residents and staff and weekly COVID-19 deaths among residents.

Statistical Research Question No. 3

Is there a significant relationship between the number of unique employees working in skilled nursing homes and the following:

number of weekly confirmed COVID-19 cases among residents and staff, and
number of weekly COVID-19 deaths among residents.

Note that this study on COVID-19 looked into three variables, namely 1) number of unique employees working in skilled nursing homes, 2) number of weekly confirmed cases among residents and staff, and 3) number of weekly COVID-19 deaths among residents.

We call the variable number of unique employees the independent variable , and the other two variables ( number of weekly confirmed cases among residents and staff and number of weekly COVID-19 deaths among residents ) as the dependent variables .

This correlation study determined if the number of staff members in nursing homes influences the number of COVID-19 cases and deaths. It aims to understand if staffing has got to do with the transmission of the deadly coronavirus. Thus, the study’s outcome could inform policy on staffing in nursing homes during the pandemic.

A simple Pearson test may be used to correlate one variable with another variable. But the study used multiple variables. Hence, they produced regression models that show how multiple variables affect the outcome. Some of the variables in the study may be redundant, meaning, those variables may represent the same attribute of a population. Stepwise multiple regression models take care of those redundancies. Using this statistical test requires further study and experience.

Source: McGarry et al. (2021)

Topic 4: Surrounding Greenness, Stress, and Memory

Scientific evidence has shown that surrounding greenness has multiple health-related benefits. Health benefits include better cognitive functioning or better intellectual activity such as thinking, reasoning, or remembering things. These findings, however, are not well understood. A study, therefore, analyzed the relationship between surrounding greenness and memory performance, with stress as a mediating variable.

Statistical Research Question No. 4

Is there a significant relationship between exposure to and use of natural environments, stress, and memory performance?

As this article is behind a paywall and we cannot see the full article, we can content ourselves with the knowledge that three major variables were explored in this study. These are 1) exposure to and use of natural environments, 2) stress, and 3) memory performance.

Referring to the abstract of this study, exposure to and use of natural environments as a variable of the study may be measured in terms of the days spent by the respondent in green surroundings. That will be a ratio variable as we can count it and has an absolute zero point. Stress levels can be measured using standardized instruments like the Perceived Stress Scale . The third variable, i.e., memory performance in terms of short-term, working memory, and overall memory may be measured using a variety of memory assessment tools as described by Murray (2016) .

As you become more familiar and well-versed in identifying the variables you would like to investigate in your study, reading studies like this requires reading the method or methodology section. This section will tell you how the researchers measured the variables of their study. Knowing how those variables are quantified can help you design your research and formulate the appropriate statistical research questions.

Source: Lega et al. (2021)

Topic 5: Income and Happiness

This recent finding is an interesting read and is available online. Just click on the link I provide as the source below. The study sought to determine if income plays a role in people’s happiness across three age groups: young (18-30 years), middle (31-64 years), and old (65 or older). The literature review suggests that income has a positive effect on an individual’s sense of happiness. That’s because more money increases opportunities to fulfill dreams and buy more goods and services.

Reading the abstract, we can readily identify one of the variables used in the study, i.e., money. It’s easy to count that. But for happiness, that is a largely subjective matter. Happiness varies between individuals. So how did the researcher measured happiness? As previously mentioned, we need to see the methodology portion to find out why.

If you click on the link to the full text of the paper on pages 10 and 11, you will read that the researcher measured happiness using a 10-point scale. The scale was categorized into three namely, 1) unhappy, 2) happy, and 3) very happy.

An investigation was conducted to determine if the size of nursing home staff and the number of COVID-19 cases are correlated. Specifically, they looked into the number of unique employees working daily, and the outcomes include weekly counts of confirmed COVID-19 cases among residents and staff and weekly COVID-19 deaths among residents.

Statistical Research Question No. 5

Is there a significant relationship between income and happiness?

Source: Måseide (2021)

Now the statistical test used by the researcher is, honestly, beyond me. I may be able to understand it how to use it but doing so requires further study. Although I have initially did some readings on logit models, ordered logit model and generalized ordered logit model are way beyond my self-study in statistics.

Anyhow, those variables found with asterisk (***, **, and **) on page 24 tell us that there are significant relationships between income and happiness. You just have to look at the probability values and refer to the bottom of the table for the level of significance of those relationships.

I do hope that upon reaching this part of the article, you are now well familiar on how to write statistical research questions. Practice makes perfect.

References:

Chomitz, V. R., Slining, M. M., McGowan, R. J., Mitchell, S. E., Dawson, G. F., & Hacker, K. A. (2009). Is there a relationship between physical fitness and academic achievement? Positive results from public school children in the northeastern United States. Journal of School Health , 79 (1), 30-37.

Lega, C., Gidlow, C., Jones, M., Ellis, N., & Hurst, G. (2021). The relationship between surrounding greenness, stress and memory. Urban Forestry & Urban Greening , 59 , 126974.

Måseide, H. (2021). Income and Happiness: Does the relationship vary with age?

McGarry, B. E., Gandhi, A. D., Grabowski, D. C., & Barnett, M. L. (2021). Larger Nursing Home Staff Size Linked To Higher Number Of COVID-19 Cases In 2020: Study examines the relationship between staff size and COVID-19 cases in nursing homes and skilled nursing facilities. Health Affairs, 40(8), 1261-1269.

Zapata, O. (2021). The relationship between climate conditions and consumption of bottled water: A potential link between climate change and plastic pollution. Ecological Economics, 187, 107090.

The Importance of Scoping in Research

Choosing the Right Topic: How to Find Inspiration for Your Research Paper

How to write survey questions, about the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

Department of Statistics

Research topics in probability and statistics, problem solving in mathematics and statistics is inspiring and enjoyable. but are achievements in mathematics and statistics any of use in the so-called real world , researchers in the department of statistics at warwick are developing and utilising modern statistics, mathematics, and computing to solve practical problems., examples of themes for undergraduate research projects:.

Discovering which genes can discriminate between diseased and healthy patients
Modelling and detecting asset price bubbles while they are happening and before they burst
Modelling infectious diseases and identifying localized outbreaks
Developing a fast algorithm through probabilistic modeling for compression of sound data
Automatically diagnosing diseases with large-scale image data utilizing crime data for crime prevention and optimal allocation of police resources
Predicting the outcome of elections based on exit poll data
Computed Tomography validation of complex structures in Additive Layer Manufacturing

Probability of containment for multitype branching process models for emerging epidemics

Non-stationary statistical modeling and inference for circadian oscillations for research in cancer chronotherapy

Bayesian Models of Category-Specific Emotional Brain Responses

Decision focused inference on Networked Proabilistic Systems: with applications to food security

Rotationally invariant statistics for examining the evidence from the pores in fingerprints

Dynamic Uncertainty Handling for Coherent Decision Making in Nuclear Emergency Response

Study of Key Interventions into Terrorism using Bayesian Networks

Assessing the risk of subsequent tonic-clonic seizures in patients with a history of simple or complex partial seizures

Multidimensional Markov-functional Interest Rate Models

Prospect Theory, Liquidation and the Disposition Effect

Dynamic Bradley-Terry modelling of sports tournaments

Further information on the wide range of research opportunities open to you as an Undergraduate or Postgraduate Taught student in the Department of Statistics can be found on at our Student Research Opportunities webpage.

More information about research in the Department of Statistics, both applied and theoretical, can be found at the departmental research pages .

Mathematics as bridge

The work of mathematicians and statisticians often turns out useful and essential, but typically in a less concrete manner than say the work of a scientists or a physician. David Hilbert, in his now historical address to scientists and physicians, put it this way:

"The instrument that mediates between theory and practice, between thought and observation, is mathematics; it builds the connecting bridge and makes it stronger and stronger. Thus it happens that our entire present-day culture, insofar as it rests on intellectual insight into and harnessing of nature, is founded on mathematics"

Probability and Statistics in the 21st century

Almost a century after Hilbert's words, the mathematical fundations of sciences and social sciences, and the evidence based approach in medicine are often being taken for granted. In the 21st century we are facing complex big data sets with unknown structures from manifold aspecs of the 'real world' as well as fascinating discourses about objective and subjective notions of risk and uncertainty.

Probability and statistics are mathematical disciplines for modelling and analysing theoretical and practical aspects of these burning questions.

251+ Math Research Topics [2024 Updated]

$Math research topics$

Mathematics, often dubbed as the language of the universe, holds immense significance in shaping our understanding of the world around us. It’s not just about crunching numbers or solving equations; it’s about unraveling mysteries, making predictions, and creating innovative solutions to complex problems. In this blog, we embark on a journey into the realm of math research topics, exploring various branches of mathematics and their real-world applications.

How Do You Write A Math Research Topic?

Writing a math research topic involves several steps to ensure clarity, relevance, and feasibility. Here’s a guide to help you craft a compelling math research topic:

Identify Your Interests: Start by exploring areas of mathematics that interest you. Whether it’s pure mathematics, applied mathematics, or interdisciplinary topics, choose a field that aligns with your passion and expertise.
Narrow Down Your Focus: Mathematics is a broad field, so it’s essential to narrow down your focus to a specific area or problem. Consider the scope of your research and choose a topic that is manageable within your resources and time frame.
Review Existing Literature: Conduct a thorough literature review to understand the current state of research in your chosen area. Identify gaps, controversies, or unanswered questions that could form the basis of your research topic.
Formulate a Research Question: Based on your exploration and literature review, formulate a clear and concise research question. Your research question should be specific, measurable, achievable, relevant, and time-bound (SMART).
Consider Feasibility: Assess the feasibility of your research topic in terms of available resources, data availability, and research methodologies. Ensure that your topic is realistic and achievable within the constraints of your project.
Consult with Experts: Seek feedback from mentors, advisors, or experts in the field to validate your research topic and refine your ideas. Their insights can help you identify potential challenges and opportunities for improvement.
Refine and Iterate: Refine your research topic based on feedback and further reflection. Iterate on your ideas to ensure clarity, coherence, and relevance to the broader context of mathematics research.
Craft a Title: Once you have finalized your research topic, craft a compelling title that succinctly summarizes the essence of your research. Your title should be descriptive, engaging, and reflective of the key themes of your study.
Write a Research Proposal: Develop a comprehensive research proposal outlining the background, objectives, methodology, and expected outcomes of your research. Your research proposal should provide a clear roadmap for your study and justify the significance of your research topic.

By following these steps, you can effectively write a math research topic that is well-defined, relevant, and poised to make a meaningful contribution to the field of mathematics.

251+ Math Research Topics: Beginners To Advanced

Prime Number Distribution in Arithmetic Progressions
Diophantine Equations and their Solutions
Applications of Modular Arithmetic in Cryptography
The Riemann Hypothesis and its Implications
Graph Theory: Exploring Connectivity and Coloring Problems
Knot Theory: Unraveling the Mathematics of Knots and Links
Fractal Geometry: Understanding Self-Similarity and Dimensionality
Differential Equations: Modeling Physical Phenomena and Dynamical Systems
Chaos Theory: Investigating Deterministic Chaos and Strange Attractors
Combinatorial Optimization: Algorithms for Solving Optimization Problems
Computational Complexity: Analyzing the Complexity of Algorithms
Game Theory: Mathematical Models of Strategic Interactions
Number Theory: Exploring Properties of Integers and Primes
Algebraic Topology: Studying Topological Invariants and Homotopy Theory
Analytic Number Theory: Investigating Properties of Prime Numbers
Algebraic Geometry: Geometry Arising from Algebraic Equations
Galois Theory: Understanding Field Extensions and Solvability of Equations
Representation Theory: Studying Symmetry in Linear Spaces
Harmonic Analysis: Analyzing Functions on Groups and Manifolds
Mathematical Logic: Foundations of Mathematics and Formal Systems
Set Theory: Exploring Infinite Sets and Cardinal Numbers
Real Analysis: Rigorous Study of Real Numbers and Functions
Complex Analysis: Analytic Functions and Complex Integration
Measure Theory: Foundations of Lebesgue Integration and Probability
Topological Groups: Investigating Topological Structures on Groups
Lie Groups and Lie Algebras: Geometry of Continuous Symmetry
Differential Geometry: Curvature and Topology of Smooth Manifolds
Algebraic Combinatorics: Enumerative and Algebraic Aspects of Combinatorics
Ramsey Theory: Investigating Structure in Large Discrete Structures
Analytic Geometry: Studying Geometry Using Analytic Methods
Hyperbolic Geometry: Non-Euclidean Geometry of Curved Spaces
Nonlinear Dynamics: Chaos, Bifurcations, and Strange Attractors
Homological Algebra: Studying Homology and Cohomology of Algebraic Structures
Topological Vector Spaces: Vector Spaces with Topological Structure
Representation Theory of Finite Groups: Decomposition of Group Representations
Category Theory: Abstract Structures and Universal Properties
Operator Theory: Spectral Theory and Functional Analysis of Operators
Algebraic Number Theory: Study of Algebraic Structures in Number Fields
Cryptanalysis: Breaking Cryptographic Systems Using Mathematical Methods
Discrete Mathematics: Combinatorics, Graph Theory, and Number Theory
Mathematical Biology: Modeling Biological Systems Using Mathematical Tools
Population Dynamics: Mathematical Models of Population Growth and Interaction
Epidemiology: Mathematical Modeling of Disease Spread and Control
Mathematical Ecology: Dynamics of Ecological Systems and Food Webs
Evolutionary Game Theory: Evolutionary Dynamics and Strategic Behavior
Mathematical Neuroscience: Modeling Brain Dynamics and Neural Networks
Mathematical Physics: Mathematical Models in Physical Sciences
Quantum Mechanics: Foundations and Applications of Quantum Theory
Statistical Mechanics: Statistical Methods in Physics and Thermodynamics
Fluid Dynamics: Modeling Flow of Fluids Using Partial Differential Equations
Mathematical Finance: Stochastic Models in Finance and Risk Management
Option Pricing Models: Black-Scholes Model and Beyond
Portfolio Optimization: Maximizing Returns and Minimizing Risk
Stochastic Calculus: Calculus of Stochastic Processes and Itô Calculus
Financial Time Series Analysis: Modeling and Forecasting Financial Data
Operations Research: Optimization of Decision-Making Processes
Linear Programming: Optimization Problems with Linear Constraints
Integer Programming: Optimization Problems with Integer Solutions
Network Flow Optimization: Modeling and Solving Flow Network Problems
Combinatorial Game Theory: Analysis of Games with Perfect Information
Algorithmic Game Theory: Computational Aspects of Game-Theoretic Problems
Fair Division: Methods for Fairly Allocating Resources Among Parties
Auction Theory: Modeling Auction Mechanisms and Bidding Strategies
Voting Theory: Mathematical Models of Voting Systems and Social Choice
Social Network Analysis: Mathematical Analysis of Social Networks
Algorithm Analysis: Complexity Analysis of Algorithms and Data Structures
Machine Learning: Statistical Learning Algorithms and Data Mining
Deep Learning: Neural Network Models with Multiple Layers
Reinforcement Learning: Learning by Interaction and Feedback
Natural Language Processing: Statistical and Computational Analysis of Language
Computer Vision: Mathematical Models for Image Analysis and Recognition
Computational Geometry: Algorithms for Geometric Problems
Symbolic Computation: Manipulation of Mathematical Expressions
Numerical Analysis: Algorithms for Solving Numerical Problems
Finite Element Method: Numerical Solution of Partial Differential Equations
Monte Carlo Methods: Statistical Simulation Techniques
High-Performance Computing: Parallel and Distributed Computing Techniques
Quantum Computing: Quantum Algorithms and Quantum Information Theory
Quantum Information Theory: Study of Quantum Communication and Computation
Quantum Error Correction: Methods for Protecting Quantum Information from Errors
Topological Quantum Computing: Using Topological Properties for Quantum Computation
Quantum Algorithms: Efficient Algorithms for Quantum Computers
Quantum Cryptography: Secure Communication Using Quantum Key Distribution
Topological Data Analysis: Analyzing Shape and Structure of Data Sets
Persistent Homology: Topological Invariants for Data Analysis
Mapper Algorithm: Method for Visualization and Analysis of High-Dimensional Data
Algebraic Statistics: Statistical Methods Based on Algebraic Geometry
Tropical Geometry: Geometric Methods for Studying Polynomial Equations
Model Theory: Study of Mathematical Structures and Their Interpretations
Descriptive Set Theory: Study of Borel and Analytic Sets
Ergodic Theory: Study of Measure-Preserving Transformations
Combinatorial Number Theory: Intersection of Combinatorics and Number Theory
Additive Combinatorics: Study of Additive Properties of Sets
Arithmetic Geometry: Interplay Between Number Theory and Algebraic Geometry
Proof Theory: Study of Formal Proofs and Logical Inference
Reverse Mathematics: Study of Logical Strength of Mathematical Theorems
Nonstandard Analysis: Alternative Approach to Analysis Using Infinitesimals
Computable Analysis: Study of Computable Functions and Real Numbers
Graph Theory: Study of Graphs and Networks
Random Graphs: Probabilistic Models of Graphs and Connectivity
Spectral Graph Theory: Analysis of Graphs Using Eigenvalues and Eigenvectors
Algebraic Graph Theory: Study of Algebraic Structures in Graphs
Metric Geometry: Study of Geometric Structures Using Metrics
Geometric Measure Theory: Study of Measures on Geometric Spaces
Discrete Differential Geometry: Study of Differential Geometry on Discrete Spaces
Algebraic Coding Theory: Study of Error-Correcting Codes
Information Theory: Study of Information and Communication
Coding Theory: Study of Error-Correcting Codes
Cryptography: Study of Secure Communication and Encryption
Finite Fields: Study of Fields with Finite Number of Elements
Elliptic Curves: Study of Curves Defined by Cubic Equations
Hyperelliptic Curves: Study of Curves Defined by Higher-Degree Equations
Modular Forms: Analytic Functions with Certain Transformation Properties
L-functions: Analytic Functions Associated with Number Theory
Zeta Functions: Analytic Functions with Special Properties
Analytic Number Theory: Study of Number Theoretic Functions Using Analysis
Dirichlet Series: Analytic Functions Represented by Infinite Series
Euler Products: Product Representations of Analytic Functions
Arithmetic Dynamics: Study of Iterative Processes on Algebraic Structures
Dynamics of Rational Maps: Study of Dynamical Systems Defined by Rational Functions
Julia Sets: Fractal Sets Associated with Dynamical Systems
Mandelbrot Set: Fractal Set Associated with Iterations of Complex Quadratic Polynomials
Arithmetic Geometry: Study of Algebraic Geometry Over Number Fields
Diophantine Geometry: Study of Solutions of Diophantine Equations Using Geometry
Arithmetic of Elliptic Curves: Study of Elliptic Curves Over Number Fields
Rational Points on Curves: Study of Rational Solutions of Algebraic Equations
Galois Representations: Study of Representations of Galois Groups
Automorphic Forms: Analytic Functions with Certain Transformation Properties
L-functions: Analytic Functions Associated with Automorphic Forms
Selberg Trace Formula: Tool for Studying Spectral Theory and Automorphic Forms
Langlands Program: Program to Unify Number Theory and Representation Theory
Hodge Theory: Study of Harmonic Forms on Complex Manifolds
Riemann Surfaces: One-dimensional Complex Manifolds
Shimura Varieties: Algebraic Varieties Associated with Automorphic Forms
Modular Curves: Algebraic Curves Associated with Modular Forms
Hyperbolic Manifolds: Manifolds with Constant Negative Curvature
Teichmüller Theory: Study of Moduli Spaces of Riemann Surfaces
Mirror Symmetry: Duality Between Calabi-Yau Manifolds
Kähler Geometry: Study of Hermitian Manifolds with Special Symmetries
Algebraic Groups: Linear Algebraic Groups and Their Representations
Lie Algebras: Study of Algebraic Structures Arising from Lie Groups
Representation Theory of Lie Algebras: Study of Representations of Lie Algebras
Quantum Groups: Deformation of Lie Groups and Lie Algebras
Algebraic Topology: Study of Topological Spaces Using Algebraic Methods
Homotopy Theory: Study of Continuous Deformations of Spaces
Homology Theory: Study of Algebraic Invariants of Topological Spaces
Cohomology Theory: Study of Dual Concepts to Homology Theory
Singular Homology: Homology Theory Defined Using Simplicial Complexes
Sheaf Theory: Study of Sheaves and Their Cohomology
Differential Forms: Study of Multilinear Differential Forms
De Rham Cohomology: Cohomology Theory Defined Using Differential Forms
Morse Theory: Study of Critical Points of Smooth Functions
Symplectic Geometry: Study of Symplectic Manifolds and Their Geometry
Floer Homology: Study of Symplectic Manifolds Using Pseudoholomorphic Curves
Gromov-Witten Invariants: Invariants of Symplectic Manifolds Associated with Pseudoholomorphic Curves
Mirror Symmetry: Duality Between Symplectic and Complex Geometry
Calabi-Yau Manifolds: Ricci-Flat Complex Manifolds
Moduli Spaces: Spaces Parameterizing Geometric Objects
Donaldson-Thomas Invariants: Invariants Counting Sheaves on Calabi-Yau Manifolds
Algebraic K-Theory: Study of Algebraic Invariants of Rings and Modules
Homological Algebra: Study of Homology and Cohomology of Algebraic Structures
Derived Categories: Categories Arising from Homological Algebra
Stable Homotopy Theory: Homotopy Theory with Stable Homotopy Groups
Model Categories: Categories with Certain Homotopical Properties
Higher Category Theory: Study of Higher Categories and Homotopy Theory
Higher Topos Theory: Study of Higher Categorical Structures
Higher Algebra: Study of Higher Categorical Structures in Algebra
Higher Algebraic Geometry: Study of Higher Categorical Structures in Algebraic Geometry
Higher Representation Theory: Study of Higher Categorical Structures in Representation Theory
Higher Category Theory: Study of Higher Categorical Structures
Homotopical Algebra: Study of Algebraic Structures in Homotopy Theory
Homotopical Groups: Study of Groups with Homotopical Structure
Homotopical Categories: Study of Categories with Homotopical Structure
Homotopy Groups: Algebraic Invariants of Topological Spaces
Homotopy Type Theory: Study of Foundations of Mathematics Using Homotopy Theory

In conclusion, the world of mathematics is vast and multifaceted, offering endless opportunities for exploration and discovery. Whether delving into the abstract realms of pure mathematics or applying mathematical principles to solve real-world problems, mathematicians play a vital role in advancing human knowledge and shaping the future of our world.

By embracing diverse math research topics and interdisciplinary collaborations, we can unlock new possibilities and harness the power of mathematics to address the challenges of today and tomorrow. So, let’s embark on this journey together as we unravel the mysteries of numbers and explore the boundless horizons of mathematical inquiry.

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Key facts about Americans and guns

A customer shops for a handgun at a gun store in Florida.

Guns are deeply ingrained in American society and the nation’s political debates.

The Second Amendment to the United States Constitution guarantees the right to bear arms, and about a third of U.S. adults say they personally own a gun. At the same time, in response to concerns such as rising gun death rates and mass shootings , President Joe Biden has proposed gun policy legislation that would expand on the bipartisan gun safety bill Congress passed last year.

Here are some key findings about Americans’ views of gun ownership, gun policy and other subjects, drawn primarily from a Pew Research Center survey conducted in June 2023 .

Pew Research Center conducted this analysis to summarize key facts about Americans and guns. We used data from recent Center surveys to provide insights into Americans’ views on gun policy and how those views have changed over time, as well as to examine the proportion of adults who own guns and their reasons for doing so.

The analysis draws primarily from a survey of 5,115 U.S. adults conducted from June 5 to June 11, 2023. Everyone who took part in the surveys cited 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 analysis on gun ownership , the questions used for the analysis on gun policy , and the survey’s methodology .

Additional information about the fall 2022 survey of parents and its methodology can be found at the link in the text of this post.

Measuring gun ownership in the United States comes with unique challenges. Unlike many demographic measures, there is not a definitive data source from the government or elsewhere on how many American adults own guns.

The Pew Research Center survey conducted June 5-11, 2023, on the Center’s American Trends Panel, asks about gun ownership using two separate questions to measure personal and household ownership. About a third of adults (32%) say they own a gun, while another 10% say they do not personally own a gun but someone else in their household does. These shares have changed little from surveys conducted in 2021 and 2017 . In each of those surveys, 30% reported they owned a gun.

These numbers are largely consistent with rates of gun ownership reported by Gallup , but somewhat higher than those reported by NORC’s General Social Survey . Those surveys also find only modest changes in recent years.

The FBI maintains data on background checks on individuals attempting to purchase firearms in the United States. The FBI reported a surge in background checks in 2020 and 2021, during the coronavirus pandemic. The number of federal background checks declined in 2022 and through the first half of this year, according to FBI statistics .

About four-in-ten U.S. adults say they live in a household with a gun, including 32% who say they personally own one, according to an August report based on our June survey. These numbers are virtually unchanged since the last time we asked this question in 2021.

There are differences in gun ownership rates by political affiliation, gender, community type and other factors.

Republicans and Republican-leaning independents are more than twice as likely as Democrats and Democratic leaners to say they personally own a gun (45% vs. 20%).
40% of men say they own a gun, compared with 25% of women.
47% of adults living in rural areas report personally owning a firearm, as do smaller shares of those who live in suburbs (30%) or urban areas (20%).
38% of White Americans own a gun, compared with smaller shares of Black (24%), Hispanic (20%) and Asian (10%) Americans.

A bar chart showing that nearly a third of U.S. adults say they personally own a gun.

Personal protection tops the list of reasons gun owners give for owning a firearm. About three-quarters (72%) of gun owners say that protection is a major reason they own a gun. Considerably smaller shares say that a major reason they own a gun is for hunting (32%), for sport shooting (30%), as part of a gun collection (15%) or for their job (7%).

The reasons behind gun ownership have changed only modestly since our 2017 survey of attitudes toward gun ownership and gun policies. At that time, 67% of gun owners cited protection as a major reason they owned a firearm.

A bar chart showing that nearly three-quarters of U.S. gun owners cite protection as a major reason they own a gun.

Gun owners tend to have much more positive feelings about having a gun in the house than non-owners who live with them. For instance, 71% of gun owners say they enjoy owning a gun – but far fewer non-gun owners in gun-owning households (31%) say they enjoy having one in the home. And while 81% of gun owners say owning a gun makes them feel safer, a narrower majority (57%) of non-owners in gun households say the same about having a firearm at home. Non-owners are also more likely than owners to worry about having a gun in the home (27% vs. 12%, respectively).

Feelings about gun ownership also differ by political affiliation, even among those who personally own firearms. Republican gun owners are more likely than Democratic owners to say owning a gun gives them feelings of safety and enjoyment, while Democratic owners are more likely to say they worry about having a gun in the home.

A chart showing the differences in feelings about guns between gun owners and non-owners in gun households.

Non-gun owners are split on whether they see themselves owning a firearm in the future. About half (52%) of Americans who don’t own a gun say they could never see themselves owning one, while nearly as many (47%) could imagine themselves as gun owners in the future.

Among those who currently do not own a gun:

A bar chart that shows non-gun owners are divided on whether they could see themselves owning a gun in the future.

61% of Republicans and 40% of Democrats who don’t own a gun say they would consider owning one in the future.
56% of Black non-owners say they could see themselves owning a gun one day, compared with smaller shares of White (48%), Hispanic (40%) and Asian (38%) non-owners.

Americans are evenly split over whether gun ownership does more to increase or decrease safety. About half (49%) say it does more to increase safety by allowing law-abiding citizens to protect themselves, but an equal share say gun ownership does more to reduce safety by giving too many people access to firearms and increasing misuse.

A bar chart that shows stark differences in views on whether gun ownership does more to increase or decrease safety in the U.S.

Republicans and Democrats differ on this question: 79% of Republicans say that gun ownership does more to increase safety, while a nearly identical share of Democrats (78%) say that it does more to reduce safety.

Urban and rural Americans also have starkly different views. Among adults who live in urban areas, 64% say gun ownership reduces safety, while 34% say it does more to increase safety. Among those who live in rural areas, 65% say gun ownership increases safety, compared with 33% who say it does more to reduce safety. Those living in the suburbs are about evenly split.

Americans increasingly say that gun violence is a major problem. Six-in-ten U.S. adults say gun violence is a very big problem in the country today, up 9 percentage points from spring 2022. In the survey conducted this June, 23% say gun violence is a moderately big problem, and about two-in-ten say it is either a small problem (13%) or not a problem at all (4%).

Looking ahead, 62% of Americans say they expect the level of gun violence to increase over the next five years. This is double the share who expect it to stay the same (31%). Just 7% expect the level of gun violence to decrease.

A line chart that shows a growing share of Americans say gun violence is a 'very big national problem.

A majority of Americans (61%) say it is too easy to legally obtain a gun in this country. Another 30% say the ease of legally obtaining a gun is about right, and 9% say it is too hard to get a gun. Non-gun owners are nearly twice as likely as gun owners to say it is too easy to legally obtain a gun (73% vs. 38%). Meanwhile, gun owners are more than twice as likely as non-owners to say the ease of obtaining a gun is about right (48% vs. 20%).

Partisan and demographic differences also exist on this question. While 86% of Democrats say it is too easy to obtain a gun legally, 34% of Republicans say the same. Most urban (72%) and suburban (63%) dwellers say it’s too easy to legally obtain a gun. Rural residents are more divided: 47% say it is too easy, 41% say it is about right and 11% say it is too hard.

A bar chart showing that about 6 in 10 Americans say it is too easy to legally obtain a gun in this country.

About six-in-ten U.S. adults (58%) favor stricter gun laws. Another 26% say that U.S. gun laws are about right, and 15% favor less strict gun laws. The percentage who say these laws should be stricter has fluctuated a bit in recent years. In 2021, 53% favored stricter gun laws, and in 2019, 60% said laws should be stricter.

A bar chart that shows women are more likely than men to favor stricter gun laws in the U.S.

About a third (32%) of parents with K-12 students say they are very or extremely worried about a shooting ever happening at their children’s school, according to a fall 2022 Center survey of parents with at least one child younger than 18. A similar share of K-12 parents (31%) say they are not too or not at all worried about a shooting ever happening at their children’s school, while 37% of parents say they are somewhat worried.

Among all parents with children under 18, including those who are not in school, 63% see improving mental health screening and treatment as a very or extremely effective way to prevent school shootings. This is larger than the shares who say the same about having police officers or armed security in schools (49%), banning assault-style weapons (45%), or having metal detectors in schools (41%). Just 24% of parents say allowing teachers and school administrators to carry guns in school would be a very or extremely effective approach, while half say this would be not too or not at all effective.

A pie chart that showing that 19% of K-12 parents are extremely worried about a shooting happening at their children's school.

There is broad partisan agreement on some gun policy proposals, but most are politically divisive, the June 2023 survey found . Majorities of U.S. adults in both partisan coalitions somewhat or strongly favor two policies that would restrict gun access: preventing those with mental illnesses from purchasing guns (88% of Republicans and 89% of Democrats support this) and increasing the minimum age for buying guns to 21 years old (69% of Republicans, 90% of Democrats). Majorities in both parties also oppose allowing people to carry concealed firearms without a permit (60% of Republicans and 91% of Democrats oppose this).

A dot plot showing bipartisan support for preventing people with mental illnesses from purchasing guns, but wide differences on other policies.

Republicans and Democrats differ on several other proposals. While 85% of Democrats favor banning both assault-style weapons and high-capacity ammunition magazines that hold more than 10 rounds, majorities of Republicans oppose these proposals (57% and 54%, respectively).

Most Republicans, on the other hand, support allowing teachers and school officials to carry guns in K-12 schools (74%) and allowing people to carry concealed guns in more places (71%). These proposals are supported by just 27% and 19% of Democrats, respectively.

Gun ownership is linked with views on gun policies. Americans who own guns are less likely than non-owners to favor restrictions on gun ownership, with a notable exception. Nearly identical majorities of gun owners (87%) and non-owners (89%) favor preventing mentally ill people from buying guns.

A dot plot that shows, within each party, gun owners are more likely than non-owners to favor expanded access to guns.

Within both parties, differences between gun owners and non-owners are evident – but they are especially stark among Republicans. For example, majorities of Republicans who do not own guns support banning high-capacity ammunition magazines and assault-style weapons, compared with about three-in-ten Republican gun owners.

Among Democrats, majorities of both gun owners and non-owners favor these two proposals, though support is greater among non-owners.

Note: This is an update of a post originally published on Jan. 5, 2016 .

Partisanship & Issues
Political Issues

Katherine Schaeffer is a research analyst at Pew Research Center

About 1 in 4 U.S. teachers say their school went into a gun-related lockdown in the last school year

Striking findings from 2023, for most u.s. gun owners, protection is the main reason they own a gun, gun violence widely viewed as a major – and growing – national problem, what the data says about gun deaths in the u.s., most popular.

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