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.
Improving your statistical inferencesEindhoven University of Technology
About this Course
Skills you will gain
- Likelihood Function
- Bayesian Statistics
- Statistical Inference
Syllabus - What you will learn from this course
Introduction + Frequentist Statistics
Likelihoods & Bayesian Statistics
Multiple Comparisons, Statistical Power, Pre-Registration
- 5 stars88.54%
- 4 stars9.85%
- 3 stars1.06%
- 2 stars0.26%
- 1 star0.26%
TOP REVIEWS FROM IMPROVING YOUR STATISTICAL INFERENCES
Excellent explanations. Strong examples. Helpful exercises. Highly recommended for anyone who ever has to conduct inferential statistics or read anything that reports a p value or bayes factor.
Great course to dig a bit deeper into some very useful statistical concept. 4 starts as many of the contents are not "open" as the course preaches (see Microsoft Office documents or GPower).
Easy to follow, well structured, good references, empathy of presenter. I will recomend this to other friends who made Black Belt certification and still don't have clear what the Pvalue is for.
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).
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