Is It Time to Ban the P Value?

Why and when I was reading this

I was following up on the p-value debate.

Key takeaways

  • Readers need to look beyond p-values and evaluate study quality and effect sizes instead
  • P-values largely depend on sample size, so in very large studies everything will be significant
  • P-values should be reported to a priori hypotheses

The reason people are criticizing the p-value is that it is misused, miscomputed, and misinterpreted.

A valid p-value has a distribution which is determined by sample size, reliability of sensitivity of the measures used, quality of the design and analyses, and quality of the research in general.

Even though a p-value might be well below 0.05 it may still lack clinical relevance, because in large studies even trivial differences yield low p-values.

In contrast, effect sizes remain the same regardless of sample size.

The author suggests a 3-part strategy.

Each p-value should be reported after a priori justification for its hypothesis

So a call for proper pre-registration of studies.

We shouldn’t do mass testing with p-values and pretend that all of those tests were created equal.

P-values should always be accompanied by descriptive statistics

Preferably, an effect size with confidence intervals.

Exploratory studies and tests should not report p-values