This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles.
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Improving your statistical inferences
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Instructor: Daniel Lakens
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There are 8 modules in this course
What's included
4 videos5 readings5 assignments
What's included
4 videos4 readings4 assignments
What's included
4 videos4 readings3 assignments
What's included
3 videos2 readings3 assignments
What's included
3 videos3 readings4 assignments
What's included
3 videos2 readings2 assignments
What's included
3 videos1 reading1 peer review
This module contains a practice exam and a graded exam. Both quizzes cover content from the entire course. We recommend making these exams only after you went through all the other modules.
What's included
3 assignments
Instructor
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Reviewed on Nov 18, 2017
One of the best courses I have done so far on Coursera. Fairly advanced and very helpful for (under-) grad students running experiments or working with data in general.
Reviewed on Jul 11, 2021
Solid course which taught me how to interpret p-values in a variety of contexts and taught me to not just to consider but (systematic and practical) ways of how to correct for publication bias.
Reviewed on Oct 6, 2017
This is a top-notch course. The ground (especially pitfalls) is very well covered, and useful free tools are engaged (R, G*Power, prof's own spreadsheets for calculating effect size).
Recommended if you're interested in Data Science
Eindhoven University of Technology
Johns Hopkins University
University of Leeds
Ball State University
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