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.
Offered By
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
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. 15 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. 15 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
3 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.
3 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 SESOI8m
Answer Form Assignment 2.2: Setting the SESOI Based on Resources10m
Answer Form Assignment 2.3: Equivalence Testing18m
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 Deviations12m
Answer Form Assignment 3.2: Power Analysis for ANOVA Designs20m
3 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.
3 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 Findings14m
Answer Form Assignment 4.2: Introduction to Meta-Analysis4m
Answer Form Assignment 4.3: Detecting Publication Bias14m
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You’ll be prompted to complete an application and will be notified if you are approved. Learn more.
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).
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