Statistical significance measures the reliability of a result obtained from a random experiment. We investigate the number of repetitions needed for a statistical result to have a certain significance. In the first step, we consider binomially distributed variables in the example of medication testing with fixed placebo efficacy, asking how many experiments are needed in order to achieve a significance of 95 %. In the next step, we take the probability distribution of the placebo efficacy into account, which to the best of our knowledge has not been done so far. Depending on the specifics, we show that in order to obtain identical significance, it may be necessary to perform twice as many experiments than in a setting where the placebo distribution is neglected. We proceed by considering more general probability distributions and close with comments on some erroneous assumptions on probability distributions which lead, for instance, to a trivial explanation of the fat tail.

翻译：统计意义测量随机实验结果的可靠性。 我们调查统计结果需要多少重复才能具有一定意义。 第一步, 我们考虑在固定安慰剂效果的药物测试中, 将二元分布变量作为固定安慰剂效果的药物测试示例, 询问需要多少实验才能达到95%的显著性。 下一步, 我们考虑安慰剂功效的概率分布, 并且根据我们所知的最佳程度, 迄今还没有完成。 根据具体情况, 我们表明, 为了获得同等重要性, 可能需要进行两倍于偏差分布环境的实验。 我们接下来要考虑更普遍的概率分布, 并最后评论一些错误的概率分布假设, 例如, 导致对脂肪尾部的微小解释 。