The process of hypothesis testing has an interesting analogy with a trial that helps on understanding the elements present in a formal hypothesis test in an intuitive way.
|Null hypothesis \(H_0\)||Accused of comitting a crime. It has the “presumption of innocence”, which means that it is not guilty until there is enough evidence to supporting its guilt|
|Sample \(X_1,\ldots,X_n\)||Collection of small evidences supporting innocence and guilt. These evidences contain a certain degree of uncontrollable randomness because of how they were collected and the context regarding the case|
|Statistic \(T_n\)||Summary of the evicences presented by the prosecutor and defense lawyer|
|Distribution of \(T_n\) under \(H_0\)||The judge conducting the trial. Evaluates the evidence presented by both sides and presents a verdict for \(H_0\)|
|Significance level \(\alpha\)||\(1-\alpha\) is the strength of evidences required by the judge for condemning \(H_0\). The judge allows evidences that on average condemn \(100\alpha\%\) of the innocents, due to the randomness inherent to the evidence collection process. \(\alpha=0.05\) is considered a reasonable level|
|\(p\)-value||Decision of the judge that measures the degree of compatibility, in a scale \(0\)–\(1\), of the presumption of innocence with the summary of the evidences presented. If \(p\)-value\(<\alpha\), \(H_0\) is declared guilty. Otherwise, is declared not guilty|
|\(H_0\) is rejected||\(H_0\) is declared guilty: there are strong evidences supporting its guilt|
|\(H_0\) is not rejected||\(H_0\) is declared not guilty: either is innocent or there are no enough evidences supporting its guilt|
More formally, the \(p\)-value of an hypothesis test about \(H0\) is defined as:
The \(p\)-value is the probability of obtaining a statistic more unfavourable to \(H_0\) than the observed, assuming that \(H_0\) is true.
Therefore, if the \(p\)-value is small (smaller than the chosen level \(\alpha\)), it is unlikely that the evidence against \(H_0\) is due to randomness. As a consequence, \(H_0\) is rejected. If the \(p\)-value is large (larger than \(\alpha\)), then it is more possible that the evidences against \(H_0\) are merely due to the randomness of the data. In this case, we do not reject \(H_0\).
If \(H_0\) holds, then the \(p\)-value (which is a random variable) is distributed uniformly in \((0,1)\). If \(H_0\) does not hold, then the distribution of the \(p\)-value is not uniform but concentrated at \(0\) (where the rejections of \(H_0\) take place).