  Since its debut in the 18th century, the P-value has been an important part
of hypothesis testing-based scientific discoveries. As the statistical engine
accelerates, questions are beginning to be raised, asking to what extent
scientific discoveries based on P-values are reliable and reproducible, and the
voice calling for adjusting the significance level or banning the P-value has
been increasingly heard. Inspired by these questions and discussions, here we
enquire into the useful roles and misuses of the P-value in scientific studies.
For common misuses and misinterpretations, we provide modest recommendations
for practitioners. Additionally, we compare statistical significance with
clinical relevance. In parallel, we review the Bayesian alternatives for
seeking evidence. Finally, we discuss the promises and risks of using
meta-analysis to pool P-values from multiple studies to aggregate evidence.
Taken together, the P-value underpins a useful probabilistic decision-making
system and provides evidence at a continuous scale. But its interpretation must
be contextual, considering the scientific question, experimental design
(including the model specification, sample size, and significance level),
statistical power, effect size, and reproducibility.
