Top 99+ Trending Statistics Research Topics for Students

statistics research topics

Being a statistics student, finding the best statistics research topics is quite challenging. But not anymore; find the best statistics research topics now!!!

Statistics is one of the tough subjects because it consists of lots of formulas, equations and many more. Therefore the students need to spend their time to understand these concepts. And when it comes to finding the best statistics research project for their topics, statistics students are always looking for someone to help them. 

In this blog, we will share with you the most interesting and trending statistics research topics in 2023. It will not just help you to stand out in your class but also help you to explore more about the world.

If you face any problem regarding statistics, then don’t worry. You can get the best statistics assignment help from one of our experts.

As you know, it is always suggested that you should work on interesting topics. That is why we have mentioned the most interesting research topics for college students and high school students. Here in this blog post, we will share with you the list of 99+ awesome statistics research topics.

Why Do We Need to Have Good Statistics Research Topics?

Table of Contents

Having a good research topic will not just help you score good grades, but it will also allow you to finish your project quickly. Because whenever we work on something interesting, our productivity automatically boosts. Thus, you need not invest lots of time and effort, and you can achieve the best with minimal effort and time. 

What Are Some Interesting Research Topics?

If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-

  • Literacy rate in a city.
  • Abortion and pregnancy rate in the USA.
  • Eating disorders in the citizens.
  • Parent role in self-esteem and confidence of the student.
  • Uses of AI in our daily life to business corporates.

Top 99+ Trending Statistics Research Topics For 2023

Here in this section, we will tell you more than 99 trending statistics research topics:

Sports Statistics Research Topics

  • Statistical analysis for legs and head injuries in Football.
  • Statistical analysis for shoulder and knee injuries in MotoGP.
  • Deep statistical evaluation for the doping test in sports from the past decade.
  • Statistical observation on the performance of athletes in the last Olympics.
  • Role and effect of sports in the life of the student.

Psychology Research Topics for Statistics

  • Deep statistical analysis of the effect of obesity on the student’s mental health in high school and college students.
  • Statistical evolution to find out the suicide reason among students and adults.
  • Statistics analysis to find out the effect of divorce on children in a country.
  • Psychology affects women because of the gender gap in specific country areas.
  • Statistics analysis to find out the cause of online bullying in students’ lives. 
  • In Psychology, PTSD and descriptive tendencies are discussed.
  • The function of researchers in statistical testing and probability.
  • Acceptable significance and probability thresholds in clinical Psychology.
  • The utilization of hypothesis and the role of P 0.05 for improved comprehension.
  • What types of statistical data are typically rejected in psychology?
  • The application of basic statistical principles and reasoning in psychological analysis.
  • The role of correlation is when several psychological concepts are at risk.
  • Actual case study learning and modeling are used to generate statistical reports.
  • In psychology, naturalistic observation is used as a research sample.
  • How should descriptive statistics be used to represent behavioral data sets?

Applied Statistics Research Topics

  • Does education have a deep impact on the financial success of an individual?
  • The investment in digital technology is having a meaningful return for corporations?
  • The gap of financial wealth between rich and poor in the USA.
  • A statistical approach to identify the effects of high-frequency trading in financial markets.
  • Statistics analysis to determine the impact of the multi-agent model in financial markets. 

Personalized Medicine Statistics Research Topics

  • Statistical analysis on the effect of methamphetamine on substance abusers.
  • Deep research on the impact of the Corona vaccine on the Omnicrone variant. 
  • Find out the best cancer treatment approach between orthodox therapies and alternative therapies.
  • Statistics analysis to identify the role of genes in the child’s overall immunity.
  • What factors help the patients to survive from Coronavirus .

Experimental Design Statistics Research Topics

  • Generic vs private education is one of the best for the students and has better financial return.
  • Psychology vs physiology: which leads the person not to quit their addictions?
  • Effect of breastmilk vs packed milk on the infant child overall development
  • Which causes more accidents: male alcoholics vs female alcoholics.
  • What causes the student not to reveal the cyberbullying in front of their parents in most cases. 

Easy Statistics Research Topics

  • Application of statistics in the world of data science
  • Statistics for finance: how statistics is helping the company to grow their finance
  • Advantages and disadvantages of Radar chart
  • Minor marriages in south-east Asia and African countries.
  • Discussion of ANOVA and correlation.
  • What statistical methods are most effective for active sports?
  • When measuring the correctness of college tests, a ranking statistical approach is used.
  • Statistics play an important role in Data Mining operations.
  • The practical application of heat estimation in engineering fields.
  • In the field of speech recognition, statistical analysis is used.
  • Estimating probiotics: how much time is necessary for an accurate statistical sample?
  • How will the United States population grow in the next twenty years?
  • The legislation and statistical reports deal with contentious issues.
  • The application of empirical entropy approaches with online grammar checking.
  • Transparency in statistical methodology and the reporting system of the United States Census Bureau.

Statistical Research Topics for High School

  • Uses of statistics in chemometrics
  • Statistics in business analytics and business intelligence
  • Importance of statistics in physics.
  • Deep discussion about multivariate statistics
  • Uses of Statistics in machine learning

Survey Topics for Statistics

  • Gather the data of the most qualified professionals in a specific area.
  • Survey the time wasted by the students in watching Tvs or Netflix.
  • Have a survey the fully vaccinated people in the USA 
  • Gather information on the effect of a government survey on the life of citizens
  • Survey to identify the English speakers in the world.

Statistics Research Paper Topics for Graduates

  • Have a deep decision of Bayes theorems
  • Discuss the Bayesian hierarchical models
  • Analysis of the process of Japanese restaurants. 
  • Deep analysis of Lévy’s continuity theorem
  • Analysis of the principle of maximum entropy

AP Statistics Topics

  • Discuss about the importance of econometrics
  • Analyze the pros and cons of Probit Model
  • Types of probability models and their uses
  • Deep discussion of ortho stochastic matrix
  • Find out the ways to get an adjacency matrix quickly

Good Statistics Research Topics 

  • National income and the regulation of cryptocurrency.
  • The benefits and drawbacks of regression analysis.
  • How can estimate methods be used to correct statistical differences?
  • Mathematical prediction models vs observation tactics.
  • In sociology research, there is bias in quantitative data analysis.
  • Inferential analytical approaches vs. descriptive statistics.
  • How reliable are AI-based methods in statistical analysis?
  • The internet news reporting and the fluctuations: statistics reports.
  • The importance of estimate in modeled statistics and artificial sampling.

Business Statistics Topics

  • Role of statistics in business in 2023
  • Importance of business statistics and analytics
  • What is the role of central tendency and dispersion in statistics
  • Best process of sampling business data.
  • Importance of statistics in big data.
  • The characteristics of business data sampling: benefits and cons of software solutions.
  • How may two different business tasks be tackled concurrently using linear regression analysis?
  • In economic data relations, index numbers, random probability, and correctness are all important.
  • The advantages of a dataset approach to statistics in programming statistics.
  • Commercial statistics: how should the data be prepared for maximum accuracy?

Statistical Research Topics for College Students

  • Evaluate the role of John Tukey’s contribution to statistics.
  • The role of statistics to improve ADHD treatment.
  • The uses and timeline of probability in statistics.
  • Deep analysis of Gertrude Cox’s experimental design in statistics.
  • Discuss about Florence Nightingale in statistics.
  • What sorts of music do college students prefer?
  • The Main Effect of Different Subjects on Student Performance.
  • The Importance of Analytics in Statistics Research.
  • The Influence of a Better Student in Class.
  • Do extracurricular activities help in the transformation of personalities?
  • Backbenchers’ Impact on Class Performance.
  • Medication’s Importance in Class Performance.
  • Are e-books better than traditional books?
  • Choosing aspects of a subject in college

How To Write Good Statistics Research Topics?

So, the main question that arises here is how you can write good statistics research topics. The trick is understanding the methodology that is used to collect and interpret statistical data. However, if you are trying to pick any topic for your statistics project, you must think about it before going any further. 

As a result, it will teach you about the data types that will be researched because the sample will be chosen correctly. On the other hand, your basic outline for choosing the correct topics is as follows:

  • Introduction of a problem
  • Methodology explanation and choice. 
  • Statistical research itself is in the main part (Body Part). 
  • Samples deviations and variables. 
  • Lastly, statistical interpretation is your last part (conclusion). 

Note:   Always include the sources from which you obtained the statistics data.

Top 3 Tips to Choose Good Statistics Research Topics

It can be quite easy for some students to pick a good statistics research topic without the help of an essay writer. But we know that it is not a common scenario for every student. That is why we will mention some of the best tips that will help you choose good statistics research topics for your next project. Either you are in a hurry or have enough time to explore. These tips will help you in every scenario.

1. Narrow down your research topic

We all start with many topics as we are not sure about our specific interests or niche. The initial step to picking up a good research topic for college or school students is to narrow down the research topic.

For this, you need to categorize the matter first. And then pick a specific category as per your interest. After that, brainstorm about the topic’s content and how you can make the points catchy, focused, directional, clear, and specific. 

2. Choose a topic that gives you curiosity

After categorizing the statistics research topics, it is time to pick one from the category. Don’t pick the most common topic because it will not help your grades and knowledge. Instead of it, please choose the best one, in which you have little information, or you are more likely to explore it.

In a statistics research paper, you always can explore something beyond your studies. By doing this, you will be more energetic to work on this project. And you will also feel glad to get them lots of information you were willing to have but didn’t get because of any reasons.

It will also make your professor happy to see your work. Ultimately it will affect your grades with a positive attitude.

3. Choose a manageable topic

Now you have decided on the topic, but you need to make sure that your research topic should be manageable. You will have limited time and resources to complete your project if you pick one of the deep statistics research topics with massive information.

Then you will struggle at the last moment and most probably not going to finish your project on time. Therefore, spend enough time exploring the topic and have a good idea about the time duration and resources you will use for the project. 

Statistics research topics are massive in numbers. Because statistics operations can be performed on anything from our psychology to our fitness. Therefore there are lots more statistics research topics to explore. But if you are not finding it challenging, then you can take the help of our statistics experts . They will help you to pick the most interesting and trending statistics research topics for your projects. 

With this help, you can also save your precious time to invest it in something else. You can also come up with a plethora of topics of your choice and we will help you to pick the best one among them. Apart from that, if you are working on a project and you are not sure whether that is the topic that excites you to work on it or not. Then we can also help you to clear all your doubts on the statistics research topic. 

Frequently Asked Questions

Q1. what are some good topics for the statistics project.

Have a look at some good topics for statistics projects:- 1. Research the average height and physics of basketball players. 2. Birth and death rate in a specific city or country. 3. Study on the obesity rate of children and adults in the USA. 4. The growth rate of China in the past few years 5. Major causes of injury in Football

Q2. What are the topics in statistics?

Statistics has lots of topics. It is hard to cover all of them in a short answer. But here are the major ones: conditional probability, variance, random variable, probability distributions, common discrete, and many more. 

Q3. What are the top 10 research topics?

Here are the top 10 research topics that you can try in 2023:

1. Plant Science 2. Mental health 3. Nutritional Immunology 4. Mood disorders 5. Aging brains 6. Infectious disease 7. Music therapy 8. Political misinformation 9. Canine Connection 10. Sustainable agriculture

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

Statistical Research Topics Latest Trends & Techniques

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

What Are the Best Statistical Research Topics Worth Writing On?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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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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.

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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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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 .

Research topic idea mega list

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.

Research topic evaluator

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)
  • 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.

Get 1-On-1 Help

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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Top 100 Statistics Topics To Research In 2023

statistics topics

If you are looking for some interesting statistics topics that should work well in 2023, you have arrived at the right place. We have a list of 100 awesome statistics topics that you can use to get the inspiration you need. And did you know that all our statistics topics for project and statistics paper topics are 100% free? You can use them as you like and even reword them.

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!

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Introduction to Research Statistical Analysis: An Overview of the Basics

Christian vandever.

1 HCA Healthcare Graduate Medical Education


This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.


Statistical analysis is necessary for any research project seeking to make quantitative conclusions. The following is a primer for research-based statistical analysis. It is intended to be a high-level overview of appropriate statistical testing, while not diving too deep into any specific methodology. Some of the information is more applicable to retrospective projects, where analysis is performed on data that has already been collected, but most of it will be suitable to any type of research. This primer will help the reader understand research results in coordination with a statistician, not to perform the actual analysis. Analysis is commonly performed using statistical programming software such as R, SAS or SPSS. These allow for analysis to be replicated while minimizing the risk for an error. Resources are listed later for those working on analysis without a statistician.

After coming up with a hypothesis for a study, including any variables to be used, one of the first steps is to think about the patient population to apply the question. Results are only relevant to the population that the underlying data represents. Since it is impractical to include everyone with a certain condition, a subset of the population of interest should be taken. This subset should be large enough to have power, which means there is enough data to deliver significant results and accurately reflect the study’s population.

The first statistics of interest are related to significance level and power, alpha and beta. Alpha (α) is the significance level and probability of a type I error, the rejection of the null hypothesis when it is true. The null hypothesis is generally that there is no difference between the groups compared. A type I error is also known as a false positive. An example would be an analysis that finds one medication statistically better than another, when in reality there is no difference in efficacy between the two. Beta (β) is the probability of a type II error, the failure to reject the null hypothesis when it is actually false. A type II error is also known as a false negative. This occurs when the analysis finds there is no difference in two medications when in reality one works better than the other. Power is defined as 1-β and should be calculated prior to running any sort of statistical testing. Ideally, alpha should be as small as possible while power should be as large as possible. Power generally increases with a larger sample size, but so does cost and the effect of any bias in the study design. Additionally, as the sample size gets bigger, the chance for a statistically significant result goes up even though these results can be small differences that do not matter practically. Power calculators include the magnitude of the effect in order to combat the potential for exaggeration and only give significant results that have an actual impact. The calculators take inputs like the mean, effect size and desired power, and output the required minimum sample size for analysis. Effect size is calculated using statistical information on the variables of interest. If that information is not available, most tests have commonly used values for small, medium or large effect sizes.

When the desired patient population is decided, the next step is to define the variables previously chosen to be included. Variables come in different types that determine which statistical methods are appropriate and useful. One way variables can be split is into categorical and quantitative variables. ( Table 1 ) Categorical variables place patients into groups, such as gender, race and smoking status. Quantitative variables measure or count some quantity of interest. Common quantitative variables in research include age and weight. An important note is that there can often be a choice for whether to treat a variable as quantitative or categorical. For example, in a study looking at body mass index (BMI), BMI could be defined as a quantitative variable or as a categorical variable, with each patient’s BMI listed as a category (underweight, normal, overweight, and obese) rather than the discrete value. The decision whether a variable is quantitative or categorical will affect what conclusions can be made when interpreting results from statistical tests. Keep in mind that since quantitative variables are treated on a continuous scale it would be inappropriate to transform a variable like which medication was given into a quantitative variable with values 1, 2 and 3.

Categorical vs. Quantitative Variables

Both of these types of variables can also be split into response and predictor variables. ( Table 2 ) Predictor variables are explanatory, or independent, variables that help explain changes in a response variable. Conversely, response variables are outcome, or dependent, variables whose changes can be partially explained by the predictor variables.

Response vs. Predictor Variables

Choosing the correct statistical test depends on the types of variables defined and the question being answered. The appropriate test is determined by the variables being compared. Some common statistical tests include t-tests, ANOVA and chi-square tests.

T-tests compare whether there are differences in a quantitative variable between two values of a categorical variable. For example, a t-test could be useful to compare the length of stay for knee replacement surgery patients between those that took apixaban and those that took rivaroxaban. A t-test could examine whether there is a statistically significant difference in the length of stay between the two groups. The t-test will output a p-value, a number between zero and one, which represents the probability that the two groups could be as different as they are in the data, if they were actually the same. A value closer to zero suggests that the difference, in this case for length of stay, is more statistically significant than a number closer to one. Prior to collecting the data, set a significance level, the previously defined alpha. Alpha is typically set at 0.05, but is commonly reduced in order to limit the chance of a type I error, or false positive. Going back to the example above, if alpha is set at 0.05 and the analysis gives a p-value of 0.039, then a statistically significant difference in length of stay is observed between apixaban and rivaroxaban patients. If the analysis gives a p-value of 0.91, then there was no statistical evidence of a difference in length of stay between the two medications. Other statistical summaries or methods examine how big of a difference that might be. These other summaries are known as post-hoc analysis since they are performed after the original test to provide additional context to the results.

Analysis of variance, or ANOVA, tests can observe mean differences in a quantitative variable between values of a categorical variable, typically with three or more values to distinguish from a t-test. ANOVA could add patients given dabigatran to the previous population and evaluate whether the length of stay was significantly different across the three medications. If the p-value is lower than the designated significance level then the hypothesis that length of stay was the same across the three medications is rejected. Summaries and post-hoc tests also could be performed to look at the differences between length of stay and which individual medications may have observed statistically significant differences in length of stay from the other medications. A chi-square test examines the association between two categorical variables. An example would be to consider whether the rate of having a post-operative bleed is the same across patients provided with apixaban, rivaroxaban and dabigatran. A chi-square test can compute a p-value determining whether the bleeding rates were significantly different or not. Post-hoc tests could then give the bleeding rate for each medication, as well as a breakdown as to which specific medications may have a significantly different bleeding rate from each other.

A slightly more advanced way of examining a question can come through multiple regression. Regression allows more predictor variables to be analyzed and can act as a control when looking at associations between variables. Common control variables are age, sex and any comorbidities likely to affect the outcome variable that are not closely related to the other explanatory variables. Control variables can be especially important in reducing the effect of bias in a retrospective population. Since retrospective data was not built with the research question in mind, it is important to eliminate threats to the validity of the analysis. Testing that controls for confounding variables, such as regression, is often more valuable with retrospective data because it can ease these concerns. The two main types of regression are linear and logistic. Linear regression is used to predict differences in a quantitative, continuous response variable, such as length of stay. Logistic regression predicts differences in a dichotomous, categorical response variable, such as 90-day readmission. So whether the outcome variable is categorical or quantitative, regression can be appropriate. An example for each of these types could be found in two similar cases. For both examples define the predictor variables as age, gender and anticoagulant usage. In the first, use the predictor variables in a linear regression to evaluate their individual effects on length of stay, a quantitative variable. For the second, use the same predictor variables in a logistic regression to evaluate their individual effects on whether the patient had a 90-day readmission, a dichotomous categorical variable. Analysis can compute a p-value for each included predictor variable to determine whether they are significantly associated. The statistical tests in this article generate an associated test statistic which determines the probability the results could be acquired given that there is no association between the compared variables. These results often come with coefficients which can give the degree of the association and the degree to which one variable changes with another. Most tests, including all listed in this article, also have confidence intervals, which give a range for the correlation with a specified level of confidence. Even if these tests do not give statistically significant results, the results are still important. Not reporting statistically insignificant findings creates a bias in research. Ideas can be repeated enough times that eventually statistically significant results are reached, even though there is no true significance. In some cases with very large sample sizes, p-values will almost always be significant. In this case the effect size is critical as even the smallest, meaningless differences can be found to be statistically significant.

These variables and tests are just some things to keep in mind before, during and after the analysis process in order to make sure that the statistical reports are supporting the questions being answered. The patient population, types of variables and statistical tests are all important things to consider in the process of statistical analysis. Any results are only as useful as the process used to obtain them. This primer can be used as a reference to help ensure appropriate statistical analysis.

Funding Statement

This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity.

Conflicts of Interest

The author declares he has no conflicts of interest.

Christian Vandever is an employee of HCA Healthcare Graduate Medical Education, an organization affiliated with the journal’s publisher.

This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity. The views expressed in this publication represent those of the author(s) and do not necessarily represent the official views of HCA Healthcare or any of its affiliated entities.


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

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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.


  • 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?


  • 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.

What's Next?

Are you also learning about dynamic equilibrium in your science class? We break this sometimes tricky concept down so it's easy to understand in our complete guide to dynamic equilibrium .

Thinking about becoming a nurse practitioner? Nurse practitioners have one of the fastest growing careers in the country, and we have all the information you need to know about what to expect from nurse practitioner school .

Want to know the fastest and easiest ways to convert between Fahrenheit and Celsius? We've got you covered! Check out our guide to the best ways to convert Celsius to Fahrenheit (or vice versa).

These recommendations are based solely on our knowledge and experience. If you purchase an item through one of our links, PrepScholar may receive a commission.

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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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How To Write A Statistics Research Paper?

Haiden Malecot

Table of Contents

Statistics Research Paper

Naturally, all-encompassing information about the slightest details of the statistical paper writing cannot be stuffed into one guideline. Still, we will provide a glimpse of the basics of the stats research paper.

What is a stats research paper?

One of the main problems of stats academic research papers is that not all students understand what it is. Put it bluntly, it is an essay that provides an analysis of the gathered statistical data to induce the key points of a specified research issue. Thus, the author of the paper creates a construct of the topic by explaining the statistical data.

Writing a statistics research paper is quite challenging because the sources of data for statistical analysis are quite numerous. These are data mining, biostatistics, quality control, surveys, statistical modelling, etc.

Collecting data for the college research paper analysis is another headache. Research papers of this type call for the data taken from the most reliable and relevant sources because no indeterminate information is inadmissible here.

How to create the perfect statistics research paper example?

If you want to create the paper that can serve as a research paper writing example of well-written statistics research paper example, then here is a guideline that will help you to master this task.

Select the topic

Obviously, work can’t be written without a topic. Therefore, it is essential to come up with the theme that promises interesting statistics, and a possibility to gather enough data for the research. Access to the reliable sources of the research data is also a must.

If you are not confident about the availability of several sources concerning the chosen topic, you’d better choose something else.

Remember to jot down all the needed information for the proper referencing when you use a resource

Data collection

The duration of this stage depends on the number of data sources and the chosen methodology of the data collection. Mind that once you have chosen the method, you should stick to it. Naturally, it is essential to explain your choice of the methodology in your statistics research paper.

Outlining the paper

Creating a rough draft of the paper is your chance to save some time and nerves. Once you’ve done it, you get a clear picture of what to write about and what points should be worked through.

The intro section

This is, perhaps, the most important part of the paper. As this is the most scientific paper from all the papers you will have to write in your studies, it calls for the most logical and clear approach. Thus, your intro should consist of:

  • Opening remarks about the field of the research.
  • Credits to other researchers who worked on this theme.
  • The scientific motivation for the new research .
  • An explanation of why existing researches are not sufficient.
  • The thesis statement , aka the core idea of the text.

The body of the text (research report, as they say in statistics)

Believe it or not, but many professional writers start such papers from the body. Here you have to place the Methodology Section where you establish the methods of data collection and the results of it. Usually, all main graphs or charts are placed here as a way to convey the results. All additional materials are gathered in the appendices.

The next paragraph of the paper will be the Evaluation of the gathered data . And that’s where the knowledge on how to read statistics in a research paper can come in handy. If you have no clue how to do it, you’re in trouble, to be honest. At least, you should know three concepts: odds ratios, confidence intervals, and p values. You can start searching for them on the web or in B.S.Everitt’s Dictionary of Statistics.

And the last section of the body is Discussion . Here, as the name suggests, you have to discuss the analysis and the results of the research.

The conclusion

This section requires only several sentences where you summarise the findings and highlight the importance of the research. You may also include a suggestion on how to continue or deepen the research of the issue.

Tips on how to write a statistics paper example

Here are some life hacks and shortcuts that you may use to boost your paper:

  • Many sources where you take the statistical data , do offer it with the interpretation. Do not waste time on calculations and take the interpretation from there.
  • Visuals are the must: always include a graph, chart, or a table to visualize your words.
  • If you do not know the statistical procedure and how to interpret the results , never use it in the paper.
  • Always put the statistics at the end of the sentence.
  • If your paper requires the presentation of your calculations and you are not confident with it, ask a pro to help you.
  • Different types of statistical data require proper formatting. Cite statistics properly according to the chosen format.

…Final thoughts

We hope that our guideline on how to write a statistics paper example unveiled the mystery of writing such papers.

But, in the case you still dread stats essays, here is a sound solution: entrust your task to the professionals! Order a paper at trustworthy writing service and enjoy saved time and the great result.

Psst… You can hand this work to a writer and have a completely stress-free evening. It would take like 3 minutes. And your perfect statistics research paper would be done on time!

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Conference Dates: (In person) 9 December - 15 December, 2024


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Century of statistical ecology reviewed

Crunching numbers isn't exactly how Neil Gilbert, a postdoctoral researcher at Michigan State University, envisioned a career in ecology.

"I think it's a little funny that I'm doing this statistical ecology work because I was always OK at math, but never particularly enjoyed it," he explained. "As an undergrad, I thought, I'll be an ecologist -- that means that I can be outside, looking at birds, that sort of thing."

As it turns out," he chuckled, "ecology is a very quantitative discipline."

Now, working in the Zipkin Quantitative Ecology lab, Gilbert is the lead author on a new article in a special collection of the journal Ecology that reviews the past century of statistical ecology .

Statistical ecology, or the study of ecological systems using mathematical equations, probability and empirical data, has grown over the last century. As increasingly large datasets and complex questions took center stage in ecological research, new tools and approaches were needed to properly address them.

To better understand how statistical ecology changed over the last century, Gilbert and his fellow authors examined a selection of 36 highly cited papers on statistical ecology -- all published in Ecology since its inception in 1920.

The team's paper examines work on statistical models across a range of ecological scales from individuals to populations, communities, ecosystems and beyond. The team also reviewed publications providing practical guidance on applying models. Gilbert noted that because, "many practicing ecologists lack extensive quantitative training," such publications are key to shaping studies.

Ecology is an advantageous place for such papers, because it is one of, "the first internationally important journals in the field. It has played an outsized role in publishing important work," said lab leader Elise Zipkin, a Red Cedar Distinguished Associate Professor in the Department of Integrative Biology.

"It has a reputation of publishing some of the most influential papers on the development and application of analytical techniques from the very beginning of modern ecological research."

The team found a persistent evolution of models and concepts in the field, especially over the past few decades, driven by refinements in techniques and exponential increases in computational power.

"Statistical ecology has exploded in the last 20 to 30 years because of advances in both data availability and the continued improvement of high-performance computing clusters," Gilbert explained.

Included among the 36 reviewed papers were a landmark 1945 study by Lee R. Dice on predicting the co-occurrence of species in space -- Ecology's most highly cited paper of all time -- and an influential 2002 paper led by Darryl MacKenzie on occupancy models. Ecologists use these models to identify the range and distribution of species in an environment.

Mackenzie's work on species detection and sampling, "played an outsized role in the study of species distributions," says Zipkin. MacKenzie's paper, which was cited more than 5,400 times, spawned various software packages that are now widely used by ecologists, she explained.

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Story Source:

Materials provided by Michigan State University . Original written by Caleb Hess. Note: Content may be edited for style and length.

Journal Reference :

  • Neil A. Gilbert, Bruna R. Amaral, Olivia M. Smith, Peter J. Williams, Sydney Ceyzyk, Samuel Ayebare, Kayla L. Davis, Wendy Leuenberger, Jeffrey W. Doser, Elise F. Zipkin. A century of statistical Ecology . Ecology , 2024; DOI: 10.1002/ecy.4283

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The Economics of Infertility: Evidence from Reproductive Medicine

WHO estimates that as many as 1 in 6 individuals of reproductive age worldwide are affected by infertility. This paper uses rich administrative population-wide data from Sweden to construct and characterize the universe of infertility treatments, and to then quantify the private costs of infertility, the willingness to pay for infertility treatments, as well as the role of insurance coverage in alleviating infertility. Persistent infertility causes a long-run deterioration of mental health and couple stability, with no long-run “protective” effects (of having no child) on earnings. Despite the high private non-pecuniary cost of infertility, we estimate a relatively low revealed private willingness to pay for infertility treatment. The rate of IVF initiations drops by half when treatment is not covered by health insurance. The response to insurance is substantially more pronounced at lower income levels. At the median of the disposable income distribution, our estimates imply a willingness to pay of at most 22% of annual income for initiating an IVF treatment (or about a 30% chance of having a child). At least 40% of the response to insurance coverage can be explained by a liquidity effect rather than traditional moral hazard, implying that insurance provides an important consumption smoothing benefit in this context. We show that insurance coverage of infertility treatments determines both the total number of additional children and their allocation across the socioeconomic spectrum.

We are grateful for helpful comments from seminar participants at the University of Michigan, Stanford University, University of Zurich, the Becker Friedman Health Economics Initiative Annual Conference, and the Whistler Junior Health Economics Summit. We thank Iliriana Shala at the Research Institute for Industrial Economics for excellent research assistance. We also gratefully acknowledge support from the National Science Foundation (CAREER SES-2144072, Persson), 2022 Stanford Discovery Innovation Fund (Polyakova), the National Institute on Aging (K01AG05984301, Polyakova), and the Sweden-America Foundation (Moshfegh). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation or the National Institute on Aging. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.


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The estimates (unless otherwise noted) were derived from various data systems of the Centers for Disease Control and Prevention (CDC), Indian Health Service (IHS), Agency for Healthcare Research and Quality (AHRQ), and U.S. Census Bureau and from published research studies. Estimated percentages and total number of people with diabetes and prediabetes were derived from the National Health and Nutrition Examination Survey (NHANES), National Health Interview Survey (NHIS), IHS National Data Warehouse (NDW), Behavioral Risk Factor Surveillance System (BRFSS), United States Diabetes Surveillance System (USDSS), and U.S. resident population estimates.

Diagnosed diabetes status was determined from self-reported information provided by survey respondents. Undiagnosed diabetes was determined by measured fasting plasma glucose or A1C levels among people without self-reported diagnosed diabetes. Numbers and rates for acute and long-term complications of diabetes were derived from the National Inpatient Sample (NIS) and National Emergency Department Sample (NEDS), as well as NHIS.

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An alpha level of 0.05 was used when determining statistically significant differences between groups. Age-adjusted estimates were calculated among adults aged 18 years or older by the direct method to the 2000 U.S. Census standard population, using age groups 18–44, 45–64, and 65 years or older. Most estimates of diabetes in this report do not differentiate between type 1 and type 2 diabetes. However, as type 2 diabetes accounts for 90% to 95% of all diabetes cases, the data presented here are more likely to be characteristic of type 2 diabetes, except as noted.

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A dataset for measuring the impact of research data and their curation

  • Libby Hemphill   ORCID: 1 , 2 ,
  • Andrea Thomer 3 ,
  • Sara Lafia 1 ,
  • Lizhou Fan 2 ,
  • David Bleckley   ORCID: 1 &
  • Elizabeth Moss 1  

Scientific Data volume  11 , Article number:  442 ( 2024 ) Cite this article

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Science funders, publishers, and data archives make decisions about how to responsibly allocate resources to maximize the reuse potential of research data. This paper introduces a dataset developed to measure the impact of archival and data curation decisions on data reuse. The dataset describes 10,605 social science research datasets, their curation histories, and reuse contexts in 94,755 publications that cover 59 years from 1963 to 2022. The dataset was constructed from study-level metadata, citing publications, and curation records available through the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. The dataset includes information about study-level attributes (e.g., PIs, funders, subject terms); usage statistics (e.g., downloads, citations); archiving decisions (e.g., curation activities, data transformations); and bibliometric attributes (e.g., journals, authors) for citing publications. This dataset provides information on factors that contribute to long-term data reuse, which can inform the design of effective evidence-based recommendations to support high-impact research data curation decisions.

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Background & summary.

Recent policy changes in funding agencies and academic journals have increased data sharing among researchers and between researchers and the public. Data sharing advances science and provides the transparency necessary for evaluating, replicating, and verifying results. However, many data-sharing policies do not explain what constitutes an appropriate dataset for archiving or how to determine the value of datasets to secondary users 1 , 2 , 3 . Questions about how to allocate data-sharing resources efficiently and responsibly have gone unanswered 4 , 5 , 6 . For instance, data-sharing policies recognize that not all data should be curated and preserved, but they do not articulate metrics or guidelines for determining what data are most worthy of investment.

Despite the potential for innovation and advancement that data sharing holds, the best strategies to prioritize datasets for preparation and archiving are often unclear. Some datasets are likely to have more downstream potential than others, and data curation policies and workflows should prioritize high-value data instead of being one-size-fits-all. Though prior research in library and information science has shown that the “analytic potential” of a dataset is key to its reuse value 7 , work is needed to implement conceptual data reuse frameworks 8 , 9 , 10 , 11 , 12 , 13 , 14 . In addition, publishers and data archives need guidance to develop metrics and evaluation strategies to assess the impact of datasets.

Several existing resources have been compiled to study the relationship between the reuse of scholarly products, such as datasets (Table  1 ); however, none of these resources include explicit information on how curation processes are applied to data to increase their value, maximize their accessibility, and ensure their long-term preservation. The CCex (Curation Costs Exchange) provides models of curation services along with cost-related datasets shared by contributors but does not make explicit connections between them or include reuse information 15 . Analyses on platforms such as DataCite 16 have focused on metadata completeness and record usage, but have not included related curation-level information. Analyses of GenBank 17 and FigShare 18 , 19 citation networks do not include curation information. Related studies of Github repository reuse 20 and Softcite software citation 21 reveal significant factors that impact the reuse of secondary research products but do not focus on research data. RD-Switchboard 22 and DSKG 23 are scholarly knowledge graphs linking research data to articles, patents, and grants, but largely omit social science research data and do not include curation-level factors. To our knowledge, other studies of curation work in organizations similar to ICPSR – such as GESIS 24 , Dataverse 25 , and DANS 26 – have not made their underlying data available for analysis.

This paper describes a dataset 27 compiled for the MICA project (Measuring the Impact of Curation Actions) led by investigators at ICPSR, a large social science data archive at the University of Michigan. The dataset was originally developed to study the impacts of data curation and archiving on data reuse. The MICA dataset has supported several previous publications investigating the intensity of data curation actions 28 , the relationship between data curation actions and data reuse 29 , and the structures of research communities in a data citation network 30 . Collectively, these studies help explain the return on various types of curatorial investments. The dataset that we introduce in this paper, which we refer to as the MICA dataset, has the potential to address research questions in the areas of science (e.g., knowledge production), library and information science (e.g., scholarly communication), and data archiving (e.g., reproducible workflows).

We constructed the MICA dataset 27 using records available at ICPSR, a large social science data archive at the University of Michigan. Data set creation involved: collecting and enriching metadata for articles indexed in the ICPSR Bibliography of Data-related Literature against the Dimensions AI bibliometric database; gathering usage statistics for studies from ICPSR’s administrative database; processing data curation work logs from ICPSR’s project tracking platform, Jira; and linking data in social science studies and series to citing analysis papers (Fig.  1 ).

figure 1

Steps to prepare MICA dataset for analysis - external sources are red, primary internal sources are blue, and internal linked sources are green.

Enrich paper metadata

The ICPSR Bibliography of Data-related Literature is a growing database of literature in which data from ICPSR studies have been used. Its creation was funded by the National Science Foundation (Award 9977984), and for the past 20 years it has been supported by ICPSR membership and multiple US federally-funded and foundation-funded topical archives at ICPSR. The Bibliography was originally launched in the year 2000 to aid in data discovery by providing a searchable database linking publications to the study data used in them. The Bibliography collects the universe of output based on the data shared in each study through, which is made available through each ICPSR study’s webpage. The Bibliography contains both peer-reviewed and grey literature, which provides evidence for measuring the impact of research data. For an item to be included in the ICPSR Bibliography, it must contain an analysis of data archived by ICPSR or contain a discussion or critique of the data collection process, study design, or methodology 31 . The Bibliography is manually curated by a team of librarians and information specialists at ICPSR who enter and validate entries. Some publications are supplied to the Bibliography by data depositors, and some citations are submitted to the Bibliography by authors who abide by ICPSR’s terms of use requiring them to submit citations to works in which they analyzed data retrieved from ICPSR. Most of the Bibliography is populated by Bibliography team members, who create custom queries for ICPSR studies performed across numerous sources, including Google Scholar, ProQuest, SSRN, and others. Each record in the Bibliography is one publication that has used one or more ICPSR studies. The version we used was captured on 2021-11-16 and included 94,755 publications.

To expand the coverage of the ICPSR Bibliography, we searched exhaustively for all ICPSR study names, unique numbers assigned to ICPSR studies, and DOIs 32 using a full-text index available through the Dimensions AI database 33 . We accessed Dimensions through a license agreement with the University of Michigan. ICPSR Bibliography librarians and information specialists manually reviewed and validated new entries that matched one or more search criteria. We then used Dimensions to gather enriched metadata and full-text links for items in the Bibliography with DOIs. We matched 43% of the items in the Bibliography to enriched Dimensions metadata including abstracts, field of research codes, concepts, and authors’ institutional information; we also obtained links to full text for 16% of Bibliography items. Based on licensing agreements, we included Dimensions identifiers and links to full text so that users with valid publisher and database access can construct an enriched publication dataset.

Gather study usage data

ICPSR maintains a relational administrative database, DBInfo, that organizes study-level metadata and information on data reuse across separate tables. Studies at ICPSR consist of one or more files collected at a single time or for a single purpose; studies in which the same variables are observed over time are grouped into series. Each study at ICPSR is assigned a DOI, and its metadata are stored in DBInfo. Study metadata follows the Data Documentation Initiative (DDI) Codebook 2.5 standard. DDI elements included in our dataset are title, ICPSR study identification number, DOI, authoring entities, description (abstract), funding agencies, subject terms assigned to the study during curation, and geographic coverage. We also created variables based on DDI elements: total variable count, the presence of survey question text in the metadata, the number of author entities, and whether an author entity was an institution. We gathered metadata for ICPSR’s 10,605 unrestricted public-use studies available as of 2021-11-16 ( ).

To link study usage data with study-level metadata records, we joined study metadata from DBinfo on study usage information, which included total study downloads (data and documentation), individual data file downloads, and cumulative citations from the ICPSR Bibliography. We also gathered descriptive metadata for each study and its variables, which allowed us to summarize and append recoded fields onto the study-level metadata such as curation level, number and type of principle investigators, total variable count, and binary variables indicating whether the study data were made available for online analysis, whether survey question text was made searchable online, and whether the study variables were indexed for search. These characteristics describe aspects of the discoverability of the data to compare with other characteristics of the study. We used the study and series numbers included in the ICPSR Bibliography as unique identifiers to link papers to metadata and analyze the community structure of dataset co-citations in the ICPSR Bibliography 32 .

Process curation work logs

Researchers deposit data at ICPSR for curation and long-term preservation. Between 2016 and 2020, more than 3,000 research studies were deposited with ICPSR. Since 2017, ICPSR has organized curation work into a central unit that provides varied levels of curation that vary in the intensity and complexity of data enhancement that they provide. While the levels of curation are standardized as to effort (level one = less effort, level three = most effort), the specific curatorial actions undertaken for each dataset vary. The specific curation actions are captured in Jira, a work tracking program, which data curators at ICPSR use to collaborate and communicate their progress through tickets. We obtained access to a corpus of 669 completed Jira tickets corresponding to the curation of 566 unique studies between February 2017 and December 2019 28 .

To process the tickets, we focused only on their work log portions, which contained free text descriptions of work that data curators had performed on a deposited study, along with the curators’ identifiers, and timestamps. To protect the confidentiality of the data curators and the processing steps they performed, we collaborated with ICPSR’s curation unit to propose a classification scheme, which we used to train a Naive Bayes classifier and label curation actions in each work log sentence. The eight curation action labels we proposed 28 were: (1) initial review and planning, (2) data transformation, (3) metadata, (4) documentation, (5) quality checks, (6) communication, (7) other, and (8) non-curation work. We note that these categories of curation work are very specific to the curatorial processes and types of data stored at ICPSR, and may not match the curation activities at other repositories. After applying the classifier to the work log sentences, we obtained summary-level curation actions for a subset of all ICPSR studies (5%), along with the total number of hours spent on data curation for each study, and the proportion of time associated with each action during curation.

Data Records

The MICA dataset 27 connects records for each of ICPSR’s archived research studies to the research publications that use them and related curation activities available for a subset of studies (Fig.  2 ). Each of the three tables published in the dataset is available as a study archived at ICPSR. The data tables are distributed as statistical files available for use in SAS, SPSS, Stata, and R as well as delimited and ASCII text files. The dataset is organized around studies and papers as primary entities. The studies table lists ICPSR studies, their metadata attributes, and usage information; the papers table was constructed using the ICPSR Bibliography and Dimensions database; and the curation logs table summarizes the data curation steps performed on a subset of ICPSR studies.

Studies (“ICPSR_STUDIES”): 10,605 social science research datasets available through ICPSR up to 2021-11-16 with variables for ICPSR study number, digital object identifier, study name, series number, series title, authoring entities, full-text description, release date, funding agency, geographic coverage, subject terms, topical archive, curation level, single principal investigator (PI), institutional PI, the total number of PIs, total variables in data files, question text availability, study variable indexing, level of restriction, total unique users downloading study data files and codebooks, total unique users downloading data only, and total unique papers citing data through November 2021. Studies map to the papers and curation logs table through ICPSR study numbers as “STUDY”. However, not every study in this table will have records in the papers and curation logs tables.

Papers (“ICPSR_PAPERS”): 94,755 publications collected from 2000-08-11 to 2021-11-16 in the ICPSR Bibliography and enriched with metadata from the Dimensions database with variables for paper number, identifier, title, authors, publication venue, item type, publication date, input date, ICPSR series numbers used in the paper, ICPSR study numbers used in the paper, the Dimension identifier, and the Dimensions link to the publication’s full text. Papers map to the studies table through ICPSR study numbers in the “STUDY_NUMS” field. Each record represents a single publication, and because a researcher can use multiple datasets when creating a publication, each record may list multiple studies or series.

Curation logs (“ICPSR_CURATION_LOGS”): 649 curation logs for 563 ICPSR studies (although most studies in the subset had one curation log, some studies were associated with multiple logs, with a maximum of 10) curated between February 2017 and December 2019 with variables for study number, action labels assigned to work description sentences using a classifier trained on ICPSR curation logs, hours of work associated with a single log entry, and total hours of work logged for the curation ticket. Curation logs map to the study and paper tables through ICPSR study numbers as “STUDY”. Each record represents a single logged action, and future users may wish to aggregate actions to the study level before joining tables.

figure 2

Entity-relation diagram.

Technical Validation

We report on the reliability of the dataset’s metadata in the following subsections. To support future reuse of the dataset, curation services provided through ICPSR improved data quality by checking for missing values, adding variable labels, and creating a codebook.

All 10,605 studies available through ICPSR have a DOI and a full-text description summarizing what the study is about, the purpose of the study, the main topics covered, and the questions the PIs attempted to answer when they conducted the study. Personal names (i.e., principal investigators) and organizational names (i.e., funding agencies) are standardized against an authority list maintained by ICPSR; geographic names and subject terms are also standardized and hierarchically indexed in the ICPSR Thesaurus 34 . Many of ICPSR’s studies (63%) are in a series and are distributed through the ICPSR General Archive (56%), a non-topical archive that accepts any social or behavioral science data. While study data have been available through ICPSR since 1962, the earliest digital release date recorded for a study was 1984-03-18, when ICPSR’s database was first employed, and the most recent date is 2021-10-28 when the dataset was collected.

Curation level information was recorded starting in 2017 and is available for 1,125 studies (11%); approximately 80% of studies with assigned curation levels received curation services, equally distributed between Levels 1 (least intensive), 2 (moderately intensive), and 3 (most intensive) (Fig.  3 ). Detailed descriptions of ICPSR’s curation levels are available online 35 . Additional metadata are available for a subset of 421 studies (4%), including information about whether the study has a single PI, an institutional PI, the total number of PIs involved, total variables recorded is available for online analysis, has searchable question text, has variables that are indexed for search, contains one or more restricted files, and whether the study is completely restricted. We provided additional metadata for this subset of ICPSR studies because they were released within the past five years and detailed curation and usage information were available for them. Usage statistics including total downloads and data file downloads are available for this subset of studies as well; citation statistics are available for 8,030 studies (76%). Most ICPSR studies have fewer than 500 users, as indicated by total downloads, or citations (Fig.  4 ).

figure 3

ICPSR study curation levels.

figure 4

ICPSR study usage.

A subset of 43,102 publications (45%) available in the ICPSR Bibliography had a DOI. Author metadata were entered as free text, meaning that variations may exist and require additional normalization and pre-processing prior to analysis. While author information is standardized for each publication, individual names may appear in different sort orders (e.g., “Earls, Felton J.” and “Stephen W. Raudenbush”). Most of the items in the ICPSR Bibliography as of 2021-11-16 were journal articles (59%), reports (14%), conference presentations (9%), or theses (8%) (Fig.  5 ). The number of publications collected in the Bibliography has increased each decade since the inception of ICPSR in 1962 (Fig.  6 ). Most ICPSR studies (76%) have one or more citations in a publication.

figure 5

ICPSR Bibliography citation types.

figure 6

ICPSR citations by decade.

Usage Notes

The dataset consists of three tables that can be joined using the “STUDY” key as shown in Fig.  2 . The “ICPSR_PAPERS” table contains one row per paper with one or more cited studies in the “STUDY_NUMS” column. We manipulated and analyzed the tables as CSV files with the Pandas library 36 in Python and the Tidyverse packages 37 in R.

The present MICA dataset can be used independently to study the relationship between curation decisions and data reuse. Evidence of reuse for specific studies is available in several forms: usage information, including downloads and citation counts; and citation contexts within papers that cite data. Analysis may also be performed on the citation network formed between datasets and papers that use them. Finally, curation actions can be associated with properties of studies and usage histories.

This dataset has several limitations of which users should be aware. First, Jira tickets can only be used to represent the intensiveness of curation for activities undertaken since 2017, when ICPSR started using both Curation Levels and Jira. Studies published before 2017 were all curated, but documentation of the extent of that curation was not standardized and therefore could not be included in these analyses. Second, the measure of publications relies upon the authors’ clarity of data citation and the ICPSR Bibliography staff’s ability to discover citations with varying formality and clarity. Thus, there is always a chance that some secondary-data-citing publications have been left out of the bibliography. Finally, there may be some cases in which a paper in the ICSPSR bibliography did not actually obtain data from ICPSR. For example, PIs have often written about or even distributed their data prior to their archival in ICSPR. Therefore, those publications would not have cited ICPSR but they are still collected in the Bibliography as being directly related to the data that were eventually deposited at ICPSR.

In summary, the MICA dataset contains relationships between two main types of entities – papers and studies – which can be mined. The tables in the MICA dataset have supported network analysis (community structure and clique detection) 30 ; natural language processing (NER for dataset reference detection) 32 ; visualizing citation networks (to search for datasets) 38 ; and regression analysis (on curation decisions and data downloads) 29 . The data are currently being used to develop research metrics and recommendation systems for research data. Given that DOIs are provided for ICPSR studies and articles in the ICPSR Bibliography, the MICA dataset can also be used with other bibliometric databases, including DataCite, Crossref, OpenAlex, and related indexes. Subscription-based services, such as Dimensions AI, are also compatible with the MICA dataset. In some cases, these services provide abstracts or full text for papers from which data citation contexts can be extracted for semantic content analysis.

Code availability

The code 27 used to produce the MICA project dataset is available on GitHub at and through Zenodo with the identifier . Data manipulation and pre-processing were performed in Python. Data curation for distribution was performed in SPSS.

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We thank the ICPSR Bibliography staff, the ICPSR Data Curation Unit, and the ICPSR Data Stewardship Committee for their support of this research. This material is based upon work supported by the National Science Foundation under grant 1930645. This project was made possible in part by the Institute of Museum and Library Services LG-37-19-0134-19.

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L.H. and A.T. conceptualized the study design, D.B., E.M., and S.L. prepared the data, S.L., L.F., and L.H. analyzed the data, and D.B. validated the data. All authors reviewed and edited the manuscript.

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Hemphill, L., Thomer, A., Lafia, S. et al. A dataset for measuring the impact of research data and their curation. Sci Data 11 , 442 (2024).

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    Diabetes estimates. An alpha level of 0.05 was used when determining statistically significant differences between groups. Age-adjusted estimates were calculated among adults aged 18 years or older by the direct method to the 2000 U.S. Census standard population, using age groups 18-44, 45-64, and 65 years or older.

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