python检验是否服从正态分布

根据引用中的代码和结果，我们可以看到采用KS检验来检验数据是否服从正态分布的p值为0.7749，大于0.05的显著性水平，因此我们不能拒绝原假设。此外，引用中的代码也使用了三种统计检验方法（KS检验、normaltest和shapiro）来检验生成的样本是否服从正态分布。你可以根据自己的数据使用这些方法进行检验。<em>1</em><em>2</em><em>3</em>
#### 引用[.reference_title]
- *1* [python正态性检验：检验数据是否服从正态分布](https://blog.csdn.net/shiyuzuxiaqianli/article/details/118060492)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT0_1"}} ] [.reference_item]
- *2* [Python判断一组数据是否服从正态分布](https://blog.csdn.net/weixin_45063703/article/details/122379055)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT0_1"}} ] [.reference_item]
- *3* [Python检验样本是否服从正态分布](https://blog.csdn.net/tszupup/article/details/108432814)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT0_1"}} ] [.reference_item]
[ .reference_list ]