本文介绍了使用Python进行数据正态性检验的方法，包括Kolmogorov-Smirnov（KS）检验，Shapiro-Wilk（W）检验，以及通过Q-Q图进行的正态性检查。KS检验基于累积分布函数，用于测试样本是否符合特定理论分布或比较两个实际分布之间的显著差异。W检验是一种基于相关性的算法，计算得出的关联系数越接近1，表明数据越符合正态分布。正常性检验的结果包括Kstest的统计量和p值，ShapiroResult的统计量和p值，以及normaltest的统计量和p值。
Kolmogorov Smirnov test is based on the cumulative distribution function. It is used to test whether a distribution conforms to a certain theoretical distribution or compare whether there are significant differences between two empirical distributions. The original hypothesis is that the sample comes from a population subject to normal distribution
The full name of W test is Shapiro Wilk test, which is a correlation based algorithm. A correlation coefficient can be obtained by calculation. The closer it is to 1, the better it shows that the data fit the normal distribution. It is recommended by the national standard gb4882-85 to make the smallest type II error.
W test is a method to check whether the sample conforms to the normal distribution when the sample size is 8 ≤ n ≤ 50. (now the research has re
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