# Statistical significance

## Concept in inferential statistics / From Wikipedia, the free encyclopedia

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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 the null hypothesis is true;[4] and the *p*-value of a result, *, is the probability of obtaining a result at least as extreme, given that the null hypothesis is true.[5] The result is ***statistically significant,** by the standards of the study, when .[6][7][8][9][10][11][12] The significance level for a study is chosen before data collection, and is typically set to 5%[13] or much lower—depending on the field of study.[14]

In any experiment or observation that involves drawing a sample from a population, there is always the possibility that an observed effect would have occurred due to sampling error alone.[15][16] But if the *p*-value of an observed effect is less than (or equal to) the significance level, an investigator may conclude that the effect reflects the characteristics of the whole population,[1] thereby rejecting the null hypothesis.[17]

This technique for testing the statistical significance of results was developed in the early 20th century. The term *significance* does not imply importance here, and the term *statistical significance* is not the same as research significance, theoretical significance, or practical significance.[1][2][18][19] For example, the term clinical significance refers to the practical importance of a treatment effect.[20]