Probability of data at least as extreme as observed if the null hypothesis holds; not the probability that the hypothesis is true.The p-value is a number computed in null hypothesis testing that quantifies how compatible the observed data are with the null hypothesis. Specifically, it is the probability of obtaining a test statistic at least as extreme as the one observed, assuming the null hypothesis is true. A common misconception is that the p-value is the probability that the null hypothesis is true, or that it measures the size of an effect — it does neither. It only tells you how surprising the data would be under a specific assumption of 'no effect'.
The concept was introduced by Karl Pearson in 1900 in the context of the chi-square test, and later refined and popularized by Ronald Fisher in the 1920s as a tool for significance testing.
To compute a p-value, you first choose a test statistic (like a t-statistic or chi-square) that summarizes the data. Under the null hypothesis, this statistic follows a known probability distribution. You then locate the observed statistic on that distribution and calculate the area under the curve beyond that point — that area is the p-value. A small p-value (e.g., 0.01) means that, if the null were true, data this extreme would occur only 1% of the time, suggesting the null may be false.
P-values are used across all empirical sciences to filter results for publication and to guide decisions in fields like medicine, economics, and genetics.
In 2016, the American Statistical Association issued a formal statement warning that p-values are widely misused and misinterpreted, sparking a movement toward more robust statistical practices.


