statistics

p value

Probability of data at least as extreme as observed if the null hypothesis holds; not the probability that the hypothesis is true.
10 related articles

Description

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'.

History

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.

How it works

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.

In practice

P-values are used across all empirical sciences to filter results for publication and to guide decisions in fields like medicine, economics, and genetics.

Fun fact

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.

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Related articles · 10

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