In statistical hypothesis testing, statistical significance (or a statistically significant result) is attained whenever the observed p-value of a test statistic is less than the significance level defined for the study. The p-value is the probability of obtaining results at least as extreme as those observed, given that the null hypothesis is true. The significance level, α, is the probability of rejecting the null hypothesis, given that it is true. This statistical technique for testing the significance of results was developed in the early 20th century.

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• In statistical hypothesis testing, statistical significance (or a statistically significant result) is attained whenever the observed p-value of a test statistic is less than the significance level defined for the study. The p-value is the probability of obtaining results at least as extreme as those observed, given that the null hypothesis is true. The significance level, α, is the probability of rejecting the null hypothesis, given that it is true. This statistical technique for testing the significance of results was developed in the early 20th century. 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. But if the p-value of an observed effect is less than the significance level, an investigator may conclude that that effect reflects the characteristics of the whole population, thereby rejecting the null hypothesis. A significance level is chosen before data collection, and typically set to 5% or , depending on the field of study. The term significance does not imply importance and the term statistical significance is not the same as research, theoretical, or practical significance. For example, the term clinical significance refers to the practical importance of a treatment effect. (en)
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• In statistical hypothesis testing, statistical significance (or a statistically significant result) is attained whenever the observed p-value of a test statistic is less than the significance level defined for the study. The p-value is the probability of obtaining results at least as extreme as those observed, given that the null hypothesis is true. The significance level, α, is the probability of rejecting the null hypothesis, given that it is true. This statistical technique for testing the significance of results was developed in the early 20th century. (en)
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• Statistical significance (en)
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