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p. -value. In null-hypothesis significance testing, the -value[note 1] is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct. [2][3] A very small p -value means that such an extreme observed outcome would be very unlikely under the null hypothesis.
p-value of chi-squared distribution for different number of degrees of freedom. The p-value was introduced by Karl Pearson [6] in the Pearson's chi-squared test, where he defined P (original notation) as the probability that the statistic would be at or above a given level. This is a one-tailed definition, and the chi-squared distribution is ...
A statistical hypothesis test typically involves a calculation of a test statistic. Then a decision is made, either by comparing the test statistic to a critical value or equivalently by evaluating a p -value computed from the test statistic. Roughly 100 specialized statistical tests have been defined. [ 1 ][ 2 ]
Statistical significance. In statistical hypothesis testing, [1][2] a result has statistical significance when a result at least as "extreme" would be very infrequent if the null hypothesis were true. [3] More precisely, a study's defined significance level, denoted by , is the probability of the study rejecting the null hypothesis, given that ...
Power (statistics) In frequentist statistics, power is a measure of the ability of an experimental design and hypothesis testing setup to detect a particular effect if it is truly present. In typical use, it is a function of the test used (including the desired level of statistical significance), the assumed distribution of the test (for ...
Likelihood-ratio test. In statistics, the likelihood-ratio test is a hypothesis test that involves comparing the goodness of fit of two competing statistical models, typically one found by maximization over the entire parameter space and another found after imposing some constraint, based on the ratio of their likelihoods.
False discovery rate. In statistics, the false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the FDR, which is the expected proportion of "discoveries" (rejected null hypotheses) that are false ...
Probability density functions (pdfs) and probability mass functions are denoted by lowercase letters, e.g. , or . Cumulative distribution functions (cdfs) are denoted by uppercase letters, e.g. , or . In particular, the pdf of the standard normal distribution is denoted by , and its cdf by .