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Daniel Lakens
This course aims to help you to ask better statistical questions when performing empirical research. We will discuss how to design informative studies, both when your predictions are correct, as when your predictions are wrong. We will question norms, and reflect on how we can improve research practices to ask more interesting questions. In practical hands on assignments you will learn techniques and tools that can be immediately implemented in your own research, such as thinking about the smallest effect size you are interested in, justifying your sample size, evaluate findings in the literature while keeping publication bias into account, performing a meta-analysis, and making your analyses computationally reproducible.
Read more
This course aims to help you to ask better statistical questions when performing empirical research. We will discuss how to design informative studies, both when your predictions are correct, as when your predictions are wrong. We will question norms, and reflect on how we can improve research practices to ask more interesting questions. In practical hands on assignments you will learn techniques and tools that can be immediately implemented in your own research, such as thinking about the smallest effect size you are interested in, justifying your sample size, evaluate findings in the literature while keeping publication bias into account, performing a meta-analysis, and making your analyses computationally reproducible.
Read more
This course aims to help you to ask better statistical questions when performing empirical research. We will discuss how to design informative studies, both when your predictions are correct, as when your predictions are wrong. We will question norms, and reflect on how we can improve research practices to ask more interesting questions. In practical hands on assignments you will learn techniques and tools that can be immediately implemented in your own research, such as thinking about the smallest effect size you are interested in, justifying your sample size, evaluate findings in the literature while keeping publication bias into account, performing a meta-analysis, and making your analyses computationally reproducible.
If you have the time, it is recommended that you complete my course 'Improving Your Statistical Inferences' before enrolling in this course, although this course is completely self-contained.
Register for this course and see more details by visiting:
OpenCourser.com/course/ypudav/improving
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What's inside
Syllabus
Module 1: Improving Your Statistical Questions
One of the biggest improvements most researchers can make is to more clearly specify their statistical questions. When you perform a study, what is it you really want to know?
What are different types of questions we can ask? Which question does a hypothesis test really answer, and is this answer actually what you are interested in, or is the question you are asking more about exploration, description, or prediction? How can we make riskier predictions than null-hypothesis tests, and why is this useful?
Read more
Syllabus
Module 1: Improving Your Statistical Questions
One of the biggest improvements most researchers can make is to more clearly specify their statistical questions. When you perform a study, what is it you really want to know?
What are different types of questions we can ask? Which question does a hypothesis test really answer, and is this answer actually what you are interested in, or is the question you are asking more about exploration, description, or prediction? How can we make riskier predictions than null-hypothesis tests, and why is this useful?
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides learners with techniques and tools that are immediately applicable to their research
Emphasizes the importance of designing informative studies and justifying error rates
Addresses the issue of publication bias and provides strategies for evaluating the literature accordingly
Emphasizes the importance of computational reproducibility and scientific integrity
Taught by Daniel Lakens, who is recognized for his work in statistical inference
Suitable for researchers who want to improve their statistical questioning and research practices
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Reviews summary
Improving research questions and study design
According to learners, this course offers a highly valuable perspective on improving empirical research by focusing on asking better statistical questions and designing more informative studies. Students particularly appreciate the practical tools and techniques provided, such as determining smallest effect sizes of interest, conducting power analyses, performing meta-analyses, evaluating publication bias, and ensuring computational reproducibility. While many find the course content to be essential for researchers, some reviewers note that it can be quite challenging and seems to assume a degree of prior statistical or research knowledge, suggesting that taking the recommended prerequisite course might be beneficial for those less experienced. Overall, it is seen as a solid and insightful course for improving statistical practice.
Practical tasks aid understanding.
"Practical assignments really help solidify understanding."
"The practical exercises are well-designed and reinforce the concepts."
Covers modern practices like bias and repro.
"Good course covering crucial topics like reproducibility and publication bias."
"I appreciated the inclusion of computational reproducibility."
"The modules on publication bias and integrity were valuable additions."
Shifts perspective on statistical inquiry.
"This course fundamentally changed how I approach statistical analysis in my research. The focus on asking better questions..."
"Excellent deep dive into designing studies that actually yield useful results."
"Valuable insights on asking better questions and justifying sample size."
Offers concrete techniques for research.
"Invaluable practical tools and techniques provided, such as determining smallest effect sizes of interest... and ensuring computational reproducibility."
"The meta-analysis section was a highlight, providing concrete tools."
"I learned how to use practical tools and strategies that I could apply immediately to my work."
Pace and difficulty noted by some.
"Found this challenging... the delivery felt rushed in places, especially the simulation part."
"The pace is quite fast, so be prepared to dedicate significant time."
"While the ideas are great in theory, the practical implementation examples could have been clearer... Not for the faint of heart."
Can be difficult without background.
"It does assume some prior statistical knowledge, which wasn't always clear."
"I hadn't taken the 'Improving Statistical Inferences' course, and I think that would have helped. Useful concepts, but hard work."
"Struggled with the technical aspects... Felt like it jumped in without sufficient foundational support."
"Definitely aimed at those with some research background."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Improving Your Statistical Questions with these
activities:
Review Pre-Calculus Concepts
Show steps
Ensure you have a strong foundation in pre-calculus concepts to fully grasp the statistical methods taught in this course.
Browse courses on
Pre-Calculus
Show steps
-
Review your notes from a previous pre-calculus course.
-
Take practice quizzes and exams to test your understanding.
Review: Thinking Statistically
Show steps
Gain a foundation in statistical thinking and learn how to apply it to real-world problems.
Show steps
-
Read the book and take notes on the key points.
-
Work through the practice problems in the book.
-
Apply what you have learned to a real-world dataset.
Join a Study Group for the Course
Show steps
Enhance your understanding by working with peers and get support from others taking the course.
Show steps
-
Find a study group that fits your schedule and learning style.
-
Attend study group meetings regularly and participate actively.
-
Work together on assignments and projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Hypothesis Testing with Null Hypothesis Tests
Show steps
Improve your ability to specify your statistical questions and design informative studies.
Browse courses on
Hypothesis Testing
Show steps
-
Review the syllabus for the online course and try to identify the concepts mentioned in this activity.
-
Take notes on the key points of hypothesis testing and null hypothesis tests.
-
Work through practice problems on hypothesis testing and null hypothesis tests.
-
Apply what you have learned to a real-world dataset.
Attend a Workshop on Data Visualization
Show steps
Enhance your ability to present data and communicate research findings effectively.
Browse courses on
Data Visualization
Show steps
-
Find a workshop on data visualization that fits your schedule and interests.
-
Register for the workshop and attend all sessions.
-
Participate actively in the workshop and ask questions.
Create a Blog Post on a Statistical Concept
Show steps
Develop your understanding of statistical concepts through teaching others.
Browse courses on
Statistical Concepts
Show steps
-
Choose a statistical concept that you are familiar with.
-
Research the concept and gather information from reliable sources.
-
Write a blog post that explains the concept in a clear and concise way.
-
Share your blog post with others and get feedback.
Conduct a Meta-Analysis to Evaluate Scientific Literature
Show steps
Build your skills in evaluating scientific literature while keeping biases in mind.
Browse courses on
Meta-Analysis
Show steps
-
Identify a research question that you are interested in.
-
Search for and collect relevant studies.
-
Extract data from the studies and assess their quality.
-
Conduct a meta-analysis to synthesize the findings of the studies.
-
Interpret the results of the meta-analysis and draw conclusions.
Design and Implement a Statistical Study
Show steps
Apply the statistical methods you learn in the course to a real-world problem and complete a hands-on project.
Browse courses on
Data Analysis
Show steps
-
Identify a research question that you are interested in.
-
Design a statistical study to answer your research question.
-
Collect data and analyze it using appropriate statistical methods.
-
Write a report on your findings.
Review Pre-Calculus Concepts
Show steps
Ensure you have a strong foundation in pre-calculus concepts to fully grasp the statistical methods taught in this course.
Browse courses on
Pre-Calculus
Show steps
Show steps
Show steps
- Review your notes from a previous pre-calculus course.
- Take practice quizzes and exams to test your understanding.
Review: Thinking Statistically
Show steps
Gain a foundation in statistical thinking and learn how to apply it to real-world problems.
Show steps
Show steps
Show steps
- Read the book and take notes on the key points.
- Work through the practice problems in the book.
- Apply what you have learned to a real-world dataset.
Join a Study Group for the Course
Show steps
Enhance your understanding by working with peers and get support from others taking the course.
Show steps
Show steps
Show steps
- Find a study group that fits your schedule and learning style.
- Attend study group meetings regularly and participate actively.
- Work together on assignments and projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Hypothesis Testing with Null Hypothesis Tests
Show steps
Improve your ability to specify your statistical questions and design informative studies.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Review the syllabus for the online course and try to identify the concepts mentioned in this activity.
- Take notes on the key points of hypothesis testing and null hypothesis tests.
- Work through practice problems on hypothesis testing and null hypothesis tests.
- Apply what you have learned to a real-world dataset.
Attend a Workshop on Data Visualization
Show steps
Enhance your ability to present data and communicate research findings effectively.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find a workshop on data visualization that fits your schedule and interests.
- Register for the workshop and attend all sessions.
- Participate actively in the workshop and ask questions.
Create a Blog Post on a Statistical Concept
Show steps
Develop your understanding of statistical concepts through teaching others.
Browse courses on
Statistical Concepts
Show steps
Show steps
Show steps
- Choose a statistical concept that you are familiar with.
- Research the concept and gather information from reliable sources.
- Write a blog post that explains the concept in a clear and concise way.
- Share your blog post with others and get feedback.
Conduct a Meta-Analysis to Evaluate Scientific Literature
Show steps
Build your skills in evaluating scientific literature while keeping biases in mind.
Browse courses on
Meta-Analysis
Show steps
Show steps
Show steps
- Identify a research question that you are interested in.
- Search for and collect relevant studies.
- Extract data from the studies and assess their quality.
- Conduct a meta-analysis to synthesize the findings of the studies.
- Interpret the results of the meta-analysis and draw conclusions.
Design and Implement a Statistical Study
Show steps
Apply the statistical methods you learn in the course to a real-world problem and complete a hands-on project.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a research question that you are interested in.
- Design a statistical study to answer your research question.
- Collect data and analyze it using appropriate statistical methods.
- Write a report on your findings.
Career center
Learners who complete Improving Your Statistical Questions will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. This course can help Statisticians by providing them with the skills to design informative studies, make predictions, and communicate findings to others. These skills are essential for conducting high-quality statistical research and providing insights to stakeholders.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course can help Data Analysts by providing them with the skills to justify error rates, perform meta-analyses, and detect publication bias. These skills are essential for conducting rigorous data analysis and providing insights to stakeholders.
Data Scientist
Data Scientist
Data Scientists use scientific methods to extract knowledge and insights from data. This course can help Data Scientists by providing them with the skills to design informative studies, perform meta-analyses, and detect publication bias. These skills are essential for conducting rigorous data analysis and providing insights to stakeholders.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology and medicine. This course can help Biostatisticians by providing them with the skills to design informative studies, evaluate findings in the literature, and make their analyses computationally reproducible. These skills are essential for conducting high-quality research and providing insights to healthcare professionals.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease and other health problems in populations. This course can help Epidemiologists by providing them with the skills to design informative studies, evaluate findings in the literature, and make their analyses computationally reproducible. These skills are essential for conducting high-quality research and developing public health interventions.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior and market trends. This course can help Market Researchers by providing them with the skills to design informative studies, evaluate findings in the literature, and make their analyses computationally reproducible. These skills are essential for conducting high-quality research and providing insights to businesses.
Policy Analyst
Policy Analyst
Policy Analysts develop and evaluate policies to address public problems. This course can help Policy Analysts by providing them with the skills to design informative studies, evaluate findings in the literature, and communicate their findings to policymakers. These skills are essential for conducting high-quality research and developing effective policies.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. This course can help Economists by providing them with the skills to design informative studies, evaluate findings in the literature, and make their analyses computationally reproducible. These skills are essential for conducting high-quality research and providing insights to policymakers and businesses.
Financial Analyst
Financial Analyst
Financial Analysts evaluate the financial performance of companies and make investment recommendations. This course can help Financial Analysts by providing them with the skills to design informative studies, evaluate findings in the literature, and make their analyses computationally reproducible. These skills are essential for conducting high-quality research and providing insights to investors.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. This course can help Actuaries by providing them with the skills to design informative studies, evaluate findings in the literature, and make their analyses computationally reproducible. These skills are essential for conducting high-quality research and providing insights to insurance companies and other organizations.
Risk Manager
Risk Manager
Risk Managers identify, assess, and mitigate risks to organizations. This course can help Risk Managers by providing them with the skills to design informative studies, evaluate findings in the literature, and make their analyses computationally reproducible. These skills are essential for conducting high-quality research and developing effective risk management strategies.
Scientist
Scientist
Scientists work in a variety of fields, using scientific methods to conduct research and develop new knowledge. This course can help Scientists by providing them with the skills to design informative studies, evaluate findings in the literature, and make their analyses computationally reproducible. These skills are essential for conducting high-quality research and communicating findings to others.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems. This course can help Data Engineers by providing them with the skills to design informative studies, evaluate findings in the literature, and make their analyses computationally reproducible. These skills are essential for developing high-quality data systems and ensuring that data is accurate and reliable.
Researcher
Researcher
Researchers conduct studies to answer questions and contribute to new knowledge. This course can help Researchers by providing them with the skills to design informative studies, evaluate findings in the literature, and make their analyses computationally reproducible. These skills are essential for conducting high-quality research and communicating findings to others.
Professor
Professor
Professors teach and conduct research at colleges and universities. This course can help Professors by providing them with the skills to design informative studies, evaluate findings in the literature, and make their analyses computationally reproducible. These skills are essential for conducting high-quality research and teaching students about statistical methods.
For more career information including salaries, visit:
OpenCourser.com/course/ypudav/improving
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Improving Your Statistical Questions.
A classic work on statistical learning that covers a wide range of topics, including linear and logistic regression, tree-based methods, and support vector machines. Provides a solid foundation in machine learning techniques.
A comprehensive guide to Bayesian statistics with a focus on computational methods. Covers topics such as Bayesian inference, model building, and simulation methods, using R and Stan. Provides a solid foundation in Bayesian statistics and its applications.
A comprehensive guide to causal inference in statistics, covering topics such as causal diagrams, counterfactuals, and structural equation modeling. Provides a solid foundation in causal inference and its applications.
A comprehensive guide to statistical methods used in psychology, covering both basic and advanced techniques. Provides clear explanations and examples, and includes exercises and SPSS syntax for hands-on practice.
A comprehensive guide to Bayesian data analysis, covering topics such as Bayesian inference, model building, and simulation methods. Provides a solid foundation in Bayesian statistics and its applications.
A comprehensive guide to statistical inference, covering topics such as point estimation, hypothesis testing, and interval estimation. Provides a solid foundation in statistical inference and its applications.
A comprehensive guide to mathematical statistics and data analysis, covering topics such as probability theory, statistical inference, and linear models. Provides a solid foundation in mathematical statistics and its applications.
A practical guide to econometric methods, covering topics such as regression analysis, instrumental variables, and difference-in-differences. Provides clear explanations and examples, and includes exercises for hands-on practice.
A comprehensive guide to linear statistical models, covering topics such as regression analysis, analysis of variance, and generalized linear models. Provides a solid foundation in linear statistical models and their applications.
A comprehensive guide to power analysis for behavioral sciences, covering topics such as effect size estimation, sample size determination, and power analysis for different statistical tests. Provides practical guidance and tools for planning and conducting research.
A comprehensive guide to generalized linear models, covering topics such as logistic regression, Poisson regression, and negative binomial regression. Provides a solid foundation in generalized linear models and their applications.
A practical guide to Bayesian data analysis using R, JAGS, and Stan. Provides hands-on guidance and examples for conducting Bayesian analyses, including model specification, inference, and diagnostics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ypudav/improving
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Effort
5 weeks of study, 2 - 4 hours/week
Level
Intermediate
Via
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Institution
Eindhoven University of Technology
Instructor
Daniel Lakens
Language
English
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