Improving Your Statistical Questions

Instructor: Daniel Lakens

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10,102 already enrolled

6 modules

Gain insight into a topic and learn the fundamentals.

112 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

112 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll 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'll gain

Tools you'll learn

R Programming

Details to know

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Assessments

12 assignments

Taught in English

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There are 6 modules in this course

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.

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?

What's included

3 videos2 readings3 assignments

3 videos Total 40 minutes

Lecture 1.1: Improving Your Statistical Questions 12 minutes
Lecture 1.2: Do You Really Want to Test a Hypothesis? 15 minutes
Lecture 1.3: Risky Predictions 13 minutes

2 readings Total 40 minutes

Download Course Materials and Course Structure (Must Read) 10 minutes
Assignment 1.1: Testing Range Predictions 30 minutes

3 assignments Total 17 minutes

Answer Form Assignment 1.1: Testing Range Predictions 2 minutes
Consent Form for Use of Data 10 minutes
Welcome: Short Survey 5 minutes

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.

What's included

3 videos3 readings3 assignments

3 videos Total 46 minutes

Lecture 2.1: Falsifying Predictions in Theory 16 minutes
Lecture 2.2: Setting the Smallest Effect Size Of Interest 14 minutes
Lecture 2.3: Falsifying Predictions in Practice 16 minutes

3 readings Total 90 minutes

Assignment 2.1: The Small Telescopes Approach to Setting a SESOI 30 minutes
Assignment 2.2: Setting the SESOI Based on Resources 30 minutes
Assignment 2.3: Equivalence Testing 30 minutes

3 assignments Total 90 minutes

Answer Form Assignment 2.1: The Small Telescopes Approach to Setting a SESOI 30 minutes
Answer Form Assignment 2.2: Setting the SESOI Based on Resources 30 minutes
Answer Form Assignment 2.3: Equivalence Testing 30 minutes

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.

What's included

3 videos2 readings2 assignments

3 videos Total 48 minutes

Lecture 3.1: Justifying Error Rates 19 minutes
Lecture 3.2: Power Analysis 13 minutes
Lecture 3.3: Simulation 15 minutes

2 readings Total 90 minutes

Assignment 3.1: Confidence Intervals for Standard Deviations 30 minutes
Assignment 3.2: Power Analysis for ANOVA Designs 60 minutes

2 assignments Total 60 minutes

Answer Form Assignment 3.1: Confidence Intervals for Standard Deviations 30 minutes
Answer Form Assignment 3.2: Power Analysis for ANOVA Designs 30 minutes

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.

What's included

3 videos4 readings3 assignments

3 videos Total 48 minutes

Lecture 4.1: Mixed Results 15 minutes
Lecture 4.2: Intro to Meta-Analysis 17 minutes
Lecture 4.3: Bias Detection 15 minutes

4 readings Total 115 minutes

Assignment 4.1: Likelihood of Significant Findings 30 minutes
Assignment 4.2: Introduction to Meta-Analysis 30 minutes
Assignment 4.3: Detecting Publication Bias 45 minutes
Assignment 4.4: Checking Your Stats 10 minutes

3 assignments Total 90 minutes

Answer Form Assignment 4.1: Likelihood of Significant Findings 30 minutes
Answer Form Assignment 4.2: Introduction to Meta-Analysis 30 minutes
Answer Form Assignment 4.3: Detecting Publication Bias 30 minutes

We discuss three last topics. First, we will make sure other people can use your data to ask new questions, by making sure your data analysis is computationally reproducible. Then, we will reflect on how your philosophy of science influences the types of questions you will ask, and what you value as you do research. Finally, we discuss scientific integrity, and reflect on why our research practice is not always aligned with the best possible ways to provide reliable answers to scientific questions.

What's included

3 videos2 readings2 plugins

3 videos Total 45 minutes

Lecture 5.1: Computational Reproducibility 16 minutes
Lecture 5.2: Philosophy of Science in Practice 15 minutes
Lecture 5.3: Scientific Integrity in Practice 14 minutes

2 readings Total 135 minutes

Assignment 5.1: Computational Reproducibility 90 minutes
Assignment 5.2: Does Your Philosophy of Science Matter in Practice? 45 minutes

2 plugins Total 60 minutes

Assignment 5.2: Does Your Philosophy of Science Matter in Practice? 30 minutes
Assignment 5.3: Applied Research Ethics 30 minutes

This module contains a graded exam. It covers content from the entire course. We recommend making this exam only after you went through all the other modules.

What's included

1 assignment

Instructor

Instructor ratings

(28 ratings)

Daniel Lakens

Eindhoven University of Technology

2 Courses 82,751 learners

Offered by

Eindhoven University of Technology

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Reviewed on Dec 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.

Reviewed on Oct 30, 2019

Daniel's second course as good as the first. He does a nice job!!

Reviewed on Jan 2, 2020

Excellent! Would like only one addition, and that's a more extensive exercise on simulating data with general linear models

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