
A scientific method of proposing a tentative explanation and evaluating it through experiments or observations.Hypothesis testing is a formal procedure in statistics for deciding whether a proposed explanation about a population is supported by sample data. You start with a null hypothesis (often 'no effect' or 'no difference') and an alternative hypothesis (what you suspect is true). The goal is to assess how likely the observed data would be if the null hypothesis were true, and then either reject or fail to reject the null based on that probability.
The framework was developed by Ronald Fisher in the 1920s, with later contributions from Jerzy Neyman and Egon Pearson formalizing decision rules and error rates.
You compute a test statistic from the data, such as a t-value or chi-square value, and compare it to a distribution expected under the null hypothesis. The p-value is the probability of obtaining a result at least as extreme as the observed one, assuming the null is true. If the p-value is below a pre-chosen significance level (commonly 0.05), you reject the null, concluding the evidence favors the alternative. The procedure balances two types of errors: false positives (Type I) and false negatives (Type II).
It is used across science to validate experimental results — from clinical trials testing drug efficacy to particle physics searches for new particles, where a 5-sigma threshold corresponds to a tiny p-value.
In particle physics, the standard for claiming a discovery is a p-value of about 3×10⁻⁷, corresponding to a 5-sigma effect, far stricter than the usual 0.05 used in many other fields.









