Tag Archives: hypothesis testing

It was never meant to last, you know. Statistical measures have their heydays; permanent relevance is no guarantee. The p-value was – and still is – a tool like no other. Through the years it has been caressed and condemned, worshipped and feared, praised and slandered – all the while standing at the crossroads of almost every hypothesis testing, modeling, and prediction. Operationally, a p-value is convenient: we reject, almost mechanically, our null assumption if this value falls below certain discipline-specific thresholds like 0.01, 0.05, etc. Still, its cumbersome construction, triggering its tricky interpretation and stunning misuses, frequently lands it on the wrong side of both practitioners and stats purists. Bodies such as the American Statistical Association routinely issue caution around its use (https://doi.org/10.1080/00031305.2016.1154108). Experts have been hearing its death rattle for quite a while. The article “E-values: calibration, combination, and applications” by V. Volk and R. Wang could be the final twist of the knife. Here, the authors offer a promising alternative – the e-value – which can coexist with – and, at times, replace – its troubled ancestor.

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