Statistical Evidence in Experimental Science (SS 2022)
Welcome to the e-learning platform of this lecture!
The goal of this lecture course is to provide a detailed overview of the available approaches to the interpretation of statistical data in experimental science, including the analysis of most common fallacies and misconceptions that are common in scientific literature. In the format of an informal discussion club, we will discuss, in particular, the following topics:
- Common data fallacies that everyone needs to know
We start by considering most common statistical fallacies and “paradoxes” that can potentially distort the interpretation of experimental data, such as those that involve a lurking variable (e.g. Simpson’s paradox) or faulty logical inferences (e.g. correlation or population fallacies).
- Three paradigms of statistics
We discuss three common approaches to the statistical data interpretation, originating from
(1) Fischerian significance tests based on p-values;
(2) the Neyman-Pearson decision procedures (hypothesis testing);
(3) the evidential paradigm based on the likelihood principle.
Using simple examples, we will consider benefits and drawbacks of every approach and define their applicability range in the context of modern scientific publishing.
- Publication bias
Distortion of the scientific record can result not only from mistakes of individual researchers, but also from the way how our academic publication system operates, favouring only “significant” results. We consider several factors influencing the publication bias and discuss its consequences in modern science.
Lectures are given in English!
Here you can find the online course, consisting of the lecture and exercises.
- Created on
- 21/03/2022 at 12:33 PM
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- Last access by supervisor
- 14/04/2022 at 11:03 AM