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Improving Your Statistical Questions

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Improving Your Statistical Questions

Name: Improving Your Statistical Questions
Rating: 4.875 (112 reviews)

Instructor: Daniel Lakens

10,173 already enrolled

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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

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

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

Details to know

Shareable certificate

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Assessments

12 assignments

Taught in English

Flexible schedule

Learn at your own pace

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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 videosTotal 40 minutes

Lecture 1.1: Improving Your Statistical Questions12 minutes
Lecture 1.2: Do You Really Want to Test a Hypothesis?15 minutes
Lecture 1.3: Risky Predictions13 minutes

2 readingsTotal 40 minutes

Download Course Materials and Course Structure (Must Read)10 minutes
Assignment 1.1: Testing Range Predictions30 minutes

3 assignmentsTotal 17 minutes

Answer Form Assignment 1.1: Testing Range Predictions2 minutes
Consent Form for Use of Data10 minutes
Welcome: Short Survey5 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 videosTotal 46 minutes

Lecture 2.1: Falsifying Predictions in Theory16 minutes
Lecture 2.2: Setting the Smallest Effect Size Of Interest14 minutes
Lecture 2.3: Falsifying Predictions in Practice16 minutes

3 readingsTotal 90 minutes

Assignment 2.1: The Small Telescopes Approach to Setting a SESOI30 minutes
Assignment 2.2: Setting the SESOI Based on Resources30 minutes
Assignment 2.3: Equivalence Testing30 minutes

3 assignmentsTotal 90 minutes

Answer Form Assignment 2.1: The Small Telescopes Approach to Setting a SESOI30 minutes
Answer Form Assignment 2.2: Setting the SESOI Based on Resources30 minutes
Answer Form Assignment 2.3: Equivalence Testing30 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 videosTotal 48 minutes

Lecture 3.1: Justifying Error Rates19 minutes
Lecture 3.2: Power Analysis13 minutes
Lecture 3.3: Simulation15 minutes

2 readingsTotal 90 minutes

Assignment 3.1: Confidence Intervals for Standard Deviations30 minutes
Assignment 3.2: Power Analysis for ANOVA Designs60 minutes

2 assignmentsTotal 60 minutes

Answer Form Assignment 3.1: Confidence Intervals for Standard Deviations30 minutes
Answer Form Assignment 3.2: Power Analysis for ANOVA Designs30 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 videosTotal 48 minutes

Lecture 4.1: Mixed Results15 minutes
Lecture 4.2: Intro to Meta-Analysis17 minutes
Lecture 4.3: Bias Detection15 minutes

4 readingsTotal 115 minutes

Assignment 4.1: Likelihood of Significant Findings30 minutes
Assignment 4.2: Introduction to Meta-Analysis30 minutes
Assignment 4.3: Detecting Publication Bias45 minutes
Assignment 4.4: Checking Your Stats10 minutes

3 assignmentsTotal 90 minutes

Answer Form Assignment 4.1: Likelihood of Significant Findings30 minutes
Answer Form Assignment 4.2: Introduction to Meta-Analysis30 minutes
Answer Form Assignment 4.3: Detecting Publication Bias30 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 videosTotal 45 minutes

Lecture 5.1: Computational Reproducibility16 minutes
Lecture 5.2: Philosophy of Science in Practice15 minutes
Lecture 5.3: Scientific Integrity in Practice14 minutes

2 readingsTotal 135 minutes

Assignment 5.1: Computational Reproducibility90 minutes
Assignment 5.2: Does Your Philosophy of Science Matter in Practice?45 minutes

2 pluginsTotal 60 minutes

Assignment 5.2: Does Your Philosophy of Science Matter in Practice?30 minutes
Assignment 5.3: Applied Research Ethics30 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 Courses83,276 learners

Offered by

Eindhoven University of Technology

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Reviewed on Oct 30, 2019

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

Reviewed on Dec 18, 2023

This was the best course that I have ever taken. Professor Lakens's excellent expression and wonderful lesson plan have created a thought-provoking review. I sincerely thank him

Reviewed on Apr 30, 2024

Excellent course and the teacher! Rich study materials and hand-on assignment. Good guide for research overall.

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Frequently asked questions

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).

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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.