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
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Eindhoven University of Technology
Eindhoven University of Technology (TU/e) is a young university, founded in 1956 by industry, local government and academia. Today, their spirit of collaboration is still at the heart of the university community. We foster an open culture where everyone feels free to exchange ideas and take initiatives.
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TOP REVIEWS FROM IMPROVING YOUR STATISTICAL INFERENCES
Eye opening course. My first introduction to some of the issues surrounding p-values as well as how to better utilize them and what they truly represent. My first introduction to effect sizes as well.
Excellent course. The lecturer has written code snippets that let the students visualize the meaning and interrelationship of p-values confidence-intervals power effect-size bayesian-inference.
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.
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.
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