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Coursera: Improving your statistical inferences

 with  Daniel Lakens
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.

In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. We will experience the problems with optional stopping and learn how to prevent these problems by using sequential analyses. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses. Finally, you will learn how to examine whether the null hypothesis is true using equivalence testing and Bayesian statistics, and how to pre-register a study, and share your data on the Open Science Framework.

Syllabus

Introduction + Frequentist Statistics


Likelihoods & Bayesian Statistics


Multiple Comparisons, Statistical Power, Pre-Registration


Effect Sizes


Confidence Intervals, Sample Size Justification, P-Curve analysis


Philosophy of Science & Theory


Open Science


Final Exam
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.

1 Student
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Cost Free Online Course (Audit)
Pace Upcoming
Provider Coursera
Language English
Certificates Paid Certificate Available
Calendar 8 weeks long
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What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
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How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

1 review for Coursera's Improving your statistical inferences

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1 out of 1 people found the following review useful
6 months ago
Heinon Matti completed this course and found the course difficulty to be medium.
I encourage all my friends in research to not do anything before doing this course! The pedagogical touch is different to any stats classes I've been on or stats MOOCs I've taken. After many lectures, I was just left staring at the screen, with the phrase "I must tell everyone" repeating in my head :)
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