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.
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Improving Your Statistical Questions
Eindhoven University of TechnologyAbout this Course
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Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Flexible deadlines
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Intermediate Level
A basic knowledge of statistics and research methods is necessary. My previous MOOC 'Improving Your Statistical Inferences' is recommended.
Approx. 18 hours to complete
English
Subtitles: English
What you will learn
Ask better questions in empirical research
Design more informative studies
Evaluate the scientific literature taking bias into account
Reflect on current norms, and how you can improve your research practices
Skills you will gain
Computational ReproducibilityMeta-AnalysisExperimental DesignStatistical InferencesPhilosophy of Science
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
A basic knowledge of statistics and research methods is necessary. My previous MOOC 'Improving Your Statistical Inferences' is recommended.
Approx. 18 hours to complete
English
Subtitles: English
Offered by
Eindhoven University of Technology
Eindhoven University of Technology (TU/e) is a young university, founded in 1956 by industry, local government and academia. Today, their spirit of collaboration is still at the heart of the university community. We foster an open culture where everyone feels free to exchange ideas and take initiatives.
Syllabus - What you will learn from this course
2 hours to complete
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?
2 hours to complete
3 videos (Total 40 min), 2 readings, 3 quizzes
3 videos
Lecture 1.2: Do You Really Want to Test a Hypothesis?15m
Lecture 1.3: Risky Predictions12m
2 readings
Download Course Materials and Course Structure (Must Read)10m
Assignment 1.1: Testing Range Predictions30m
3 practice exercises
Consent Form for Use of Data10m
Welcome: Short Survey5m
Answer Form Assignment 1.1: Testing Range Predictions2m
4 hours to complete
Module 2: Falsifying Predictions
There is little use in making predictions if you can never be wrong - so how do we make sure your predictions are falsifiable? We discuss why falsifiable predictions are important, and how to make your predictions falsifiable in practice. One important aspect of making predictions falsifiable is to specify a range of values that is not predicted, and we will examine different approaches to specifying a smallest effect size of interest.
4 hours to complete
3 videos (Total 46 min), 3 readings, 3 quizzes
3 videos
Lecture 2.2: Setting the Smallest Effect Size Of Interest14m
Lecture 2.3: Falsifying Predictions in Practice15m
3 readings
Assignment 2.1: The Small Telescopes Approach to Setting a SESOI30m
Assignment 2.2: Setting the SESOI Based on Resources30m
Assignment 2.3: Equivalence Testing30m
3 practice exercises
Answer Form Assignment 2.1: The Small Telescopes Approach to Setting a SESOI30m
Answer Form Assignment 2.2: Setting the SESOI Based on Resources30m
Answer Form Assignment 2.3: Equivalence Testing30m
3 hours to complete
Module 3: Designing Informative Studies
If studies are designed to answer a question, you should make sure the answer you will get after collecting data is informative. Instead of mindlessly setting Type 1 and Type 2 error rates, we will learn why it is important to be able to justify error rates, and some approaches how to do so. We discuss the benefits of using your smallest effect size of interest in power analyses, and why learning to simulate data is a useful tool. Simulations can help you to improve your understanding of statistics, enable you to design informative studies, and even ask novel questions.
3 hours to complete
3 videos (Total 48 min), 2 readings, 2 quizzes
3 videos
Lecture 3.2: Power Analysis12m
Lecture 3.3: Simulation15m
2 readings
Assignment 3.1: Confidence Intervals for Standard Deviations30m
Assignment 3.2: Power Analysis for ANOVA Designs1h
2 practice exercises
Answer Form Assignment 3.1: Confidence Intervals for Standard Deviations30m
Answer Form Assignment 3.2: Power Analysis for ANOVA Designs30m
4 hours to complete
Module 4: Meta-Analysis and Bias Detection
Regrettably we work in a scientific enterprise where the published literature does not reflect real research. Publication bias and selection biases lead to a scientific literature that can’t be interpreted without taking these biases into account. We will discuss what real research lines look like, and how to meta-analytically evaluate the literature while keeping bias in mind.
4 hours to complete
3 videos (Total 48 min), 4 readings, 3 quizzes
3 videos
Lecture 4.2: Intro to Meta-Analysis17m
Lecture 4.3: Bias Detection15m
4 readings
Assignment 4.1: Likelihood of Significant Findings30m
Assignment 4.2: Introduction to Meta-Analysis30m
Assignment 4.3: Detecting Publication Bias45m
Assignment 4.4: Checking Your Stats10m
3 practice exercises
Answer Form Assignment 4.1: Likelihood of Significant Findings30m
Answer Form Assignment 4.2: Introduction to Meta-Analysis30m
Answer Form Assignment 4.3: Detecting Publication Bias30m
Reviews
TOP REVIEWS FROM IMPROVING YOUR STATISTICAL QUESTIONS
by HSDec 3, 2019
I recommend this course to everyone who wants to improve their grasp of statistics. The course involves content that is timely and relevant within an easy-to-digest form and amount.
by AHMay 11, 2020
Fantastic state-of-the-art and practical knowledge. It is very useful for researchers at any stage of the scientific career. Thank you, Daniel.
by SSJan 2, 2020
Excellent! Would like only one addition, and that's a more extensive exercise on simulating data with general linear models
by SWDec 31, 2019
Cracking - very informative, nice mixture of modes of learning, and engaging
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Which previous knowledge is required?
The course assumes basic knowledge about statistical inferences (t-tests, ANOVA) and some knowledge of designing research studies. The course is for intermediate level. Coursera offers basic introductions to statistics (which this course is not), and my previous MOOC 'Improving Your Statistical Inferences' might be a better starting point if you lack training in statistics. You do not need knowledge programming in R - we will use it as a fancy calculator by changing code (but not programming).
Will I earn university credit for completing the Course?
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