Tag Archive | Regression Modeling

The Reproducibility Crisis

In her insightful Nature comment Rein in the Four Horsemen of Irreproducibility, Dorothy Bishop describes how threats to reproducibility, recognized but unaddressed for decades, might finally be brought under control, by avoiding what she refers to as “the four horsemen of the reproducibility apocalypse”: publication bias, low statistical power, P-value hacking and HARKing (hypothesizing after results are known). In the video below she makes several important points. My perception is that the SCM research community does not take the reproducibility debate seriously enough.

The p-value Debate Has Reached SCM Research

We should not ignore that researchers – in general but also in supply chain management – are not always as properly trained to perform data analysis as they should be. A highly visible discussion is currently going on regarding the prevalent misuses of p-values. For example, too often research has been considered as “good” research, just because the p-value passed a specific threshold – also in the SCM discipline. But the p-value is not an interpretation, it rather needs interpretation! Some statisticians now even prefer to replace p-values with other approaches and some journals have decided to ban p-values. Based on this ongoing discussion, the influential American Statistical Association has now issued a Statement on Statistical Significance and p-values. It contains six principles underlying the proper use and interpretation of the p-value. As a discipline, we should take these principles seriously: in our own research, but also when we review the manuscripts of our colleagues.

Wasserstein, R., & Lazar, N. (2016). The ASA’s Statement on p-values: Context, Process, and Purpose. The American Statistician https://doi.org/10.1080/00031305.2016.1154108