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The Nerdiest Debate of the Century
Ah, the classic p-value. The concept of hypothesis testing and p-value is very familiar to all of us. We were first taught about it in high school and again, in the university if we took fundamental statistics. At first glance, it seems like all powerful and reliable tools that one can use to aid his/her decision making process — which can be true in certain applications. However little did I know, the dynamic duo are not infallible.
In the year 2016, a statement by American Statistical Association (ASA) centred around p values sparked one of the most important and nerdiest debate of the century¹. The crux of the problem? The reproducibility and replicability of scientific conclusions. Every scientific research has the same KPI — Does it have enough scientific evidence to support the hypotheses? It is only when there is “enough” scientific evidence that these hypotheses can regarded as the “correct” conclusions.
Classically, hypothesis testing and p-value are used in tandem as a proof that there is enough evidence to support the hypotheses. But, there are many examples where it leads to spurious conclusions instead. How could this happen? We will look at two of the most cited causes.
