Commonly used in null hypothesis testing such as t-test, ANOVA, Fisher’s exact test, etc..
It represents the probability of obtaining test resullts that are at least as extreme as the realization, assuming the null hypothesis is correct.
Common misapplications of p-values
Multiple testing
When a set of statistical inferences are considered simultaneously: the more inferences are made, the more likely erroneous inferences are to occur.
p-value hacking
Misuse of data analysis to find patterns in data that can be presented as statistically significant, thu increase false positive. (e.g. performing many statistical tests and only reporting those that come back with significant results.)