Statistical Inference

This blog is based on the book of the same name by Norbert Hirschauer, Sven Grüner, and Oliver Mußhoff that was published in SpringerBriefs in Applied Statistics and Econometrics in August 2022. Starting from the premise that a lacking understanding of the probabilistic foundations of statistical inference is responsible for the inferential errors associated with the conventional routine of null-hypothesis-significance-testing (NHST), the book provides readers with an effective intuition and conceptual understanding of statistical inference.

Like many others, I was aware that there was controversy over null-hypothesis statistical testing. Nevertheless, I was shocked to learn that leading figures in the American Statistical Association (ASA) recently called for abolishing the term “statistical significance”. In an editorial in the ASA’s flagship journal, The American Statistician, Ronald Wasserstein, Allen Schirm, and Nicole Lazar write: “Based on our review of the articles in this special issue and the broader literature, we conclude that it is time to stop using the term ‘statistically significant’ entirely.

This blog is based on the homonymous paper by Norbert Hirschauer, Sven Grüner, Oliver Mußhoff, and Claudia Becker in the Journal of Economics and Statistics. It is motivated by prevalent inferential errors and the intensifying debate on p-values – as expressed, for example in the activities of the American Statistical Association including its p-value symposium in 2017 and the March 19 Special Issue on Statistical inference in the 21st century: A world beyond P < 0.