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Florence Nightingale Colloquium presents Peter Grünwald - webinar

vrijdag 15 mei 2020
The seminar is targeted at a broad audience, in particular we invite master students, PhD candidates and supervisors interested or involved in the Data Science Research programme as well as colleagues from LIACS and MI to attend. The seminar is organized by the DSO, MI and LIACS.
Kaltura Live Room opens at 13:10.
Prof. Dr. Peter Grünwald Professor of Statistical Learning at Leiden University and Head of the Machine Learning group at CWI

Safe testing

A large fraction (some claim > 1/2) of published research in top journals in applied sciences such as medicine and psychology is irreproduceable. In light of this 'replicability crisis', standard p-value based hypothesis testing has come under intense scrutiny. One of its many problems is the following: if our test result is promising but nonconclusive (say, p = 0.07) we cannot simply decide to gather a few more data points. While this practice is ubiquitous in science, it invalidates p-values and error guarantees.
Here we propose an alternative hypothesis testing methodology based on gambling ideas. This safe testing method allows us to consider additional data and effortlessly combine results from different tests, while preserving error guarantees. While the main idea is in itself not new, in the past it could essentially only be applied to problems with a 'simple' null hypothesis (no free parameters, e.g. testing whether a coin is fair). Yet nearly all  tests used in practice, such as the t-test or independence tests, have nonsimple, 'composite' null hypotheses (i.e. free parameters). Our breakthrough: we found  a method to construct safe tests for arbitrary composite testing scenarios. This allows us to formulate safe versions of some of the most popular tests used in practice;  an R package for 'safe' t- and 2x2 testing is already available.
We end the talk by briefly reviewing the three main paradigms of testing: Fisherian, Neymanian, and Bayesian.  Even now, a hundred years after the inception of modern statistics, there is no consensus on which one is 'right'.  It turns out that, unlike currently used tests, safe tests have a valid interpretation within all three paradigms.

Joint Work with R. de Heide, W. Koolen, J. ter Schure, A. Ly, R. Turner

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