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In statistical hypothesis testing, statistical significance (or a statistically significant result) is attained when a ''p''-value is less than the significance level (denoted α, alpha). The ''p''-value is the probability of obtaining at least as extreme results given that the null hypothesis is true whereas the significance level α is the probability of rejecting the null hypothesis given that it is true. Equivalently, when the null hypothesis specifies the value of a parameter, the data are said to be statistically significant at given confidence level γ = 1 − α when the computed confidence interval for that parameter ''fails'' to contain the value specified by the null hypothesis.〔"Conclusions about statistical significance are possible with the help of the confidence interval. If the confidence interval does not include the value of zero effect, it can be assumed that there is a statistically significant result." 〕〔(StatNews #73: Overlapping Confidence Intervals and Statistical Significance )〕 As a matter of good scientific practice, a significance level is chosen before data collection and is often set to 0.05 (5%). Other significance levels (e.g., 0.01) may be used, depending on the field of study. 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 is less than the significance level (e.g., ''p'' < 0.05), then an investigator may conclude that the observed effect actually reflects the characteristics of the population rather than just sampling error.〔 Investigators may then report that the result attains statistical significance, thereby rejecting the null hypothesis. The present-day concept of statistical significance originated with Ronald Fisher when he developed statistical hypothesis testing based on ''p''-values in the early 20th century. It was Jerzy Neyman and Egon Pearson who later recommended that the significance level be set ahead of time, prior to any data collection.〔 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. ==History== (詳細はRonald Fisher when he developed statistical hypothesis testing, which he described as "tests of significance", in his 1925 publication ''Statistical Methods for Research Workers''.〔〔〔 Fisher suggested a probability of one in twenty (0.05) as a convenient cutoff level to reject the null hypothesis. In their 1933 paper, Jerzy Neyman and Egon Pearson recommended that the significance level (e.g. 0.05), which they called α, be set ahead of time, prior to any data collection.〔 Despite his initial suggestion of 0.05 as a significance level, Fisher did not intend this cutoff value to be fixed, and in his 1956 publication ''Statistical methods and scientific inference'' he recommended that significant levels be set according to specific circumstances.〔 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Statistical significance」の詳細全文を読む スポンサード リンク
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