非正态分布-->正态：
在将非正态分布的数据转换为正态分布的数据时，首先根据偏度、峰度等情况进行判断，不同的情况可以使用对数、开根号等转换方法：
http://blog.sina.com.cn/s/blog_13ec735f50102x59u.html
https://www.cnblogs.com/minks/p/6259883.html
http://blog.sina.com.cn/s/blog_751bd9440102vy6j.html
python-检验33品种数据是否是正态分布———————https://blog.csdn.net/qq_26948675/article/details/73648910（根据样本数选择合适的检验分布方法）；http://blog.sina.com.cn/s/blog_80828ce00101oiso.html
matlab判断是否服从正态分布：https://wenku.baidu.com/view/035d6bfadd3383c4bb4cd291.html
判断数据是否服从正态分布方法：
https://blog.csdn.net/QimaoRyan/article/details/72861387 CSDN方法
https://wenku.baidu.com/view/043521325a8102d276a22f51.html
****************https://www.cnblogs.com/webRobot/p/6760839.html************
# -*- coding: utf-8 -*-\'\'\'Author：TobyQQ：231469242，all right reversed,no commercial use\'\'\'import scipy
from scipy.stats import f
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
# additional packagesfrom statsmodels.stats.diagnostic import lillifors
group1=[2,3,7,2,6]
group2=[10,8,7,5,10]
group3=[10,13,14,13,15]
list_groups=[group1,group2,group3]
list_total=group1+group2+group3
#正态分布测试def check_normality(testData):
#20<样本数<50用normal test算法检验正态分布性
if 20<len(testData) <50:
p_value= stats.normaltest(testData)[1]
if p_value<0.05:
print"use normaltest"
print "data are not normal distributed"
return False
else:
print"use normaltest"
print "data are normal distributed"
return True
#样本数小于50用Shapiro-Wilk算法检验正态分布性
if len(testData) <50:
p_value= stats.shapiro(testData)[1]
if p_value<0.05:
print "use shapiro:"
print "data are not normal distributed"
return False
else:
print "use shapiro:"
print "data are normal distributed"
return True
if 300>=len(testData) >=50:
p_value= lillifors(testData)[1]
if p_value<0.05:
print "use lillifors:"
print "data are not normal distributed"
return False
else:
print "use lillifors:"
print "data are normal distributed"
return True
if len(testData) >300:
p_value= stats.kstest(testData,\'norm\')[1]
if p_value<0.05:
print "use kstest:"
print "data are not normal distributed"
return False
else:
print "use kstest:"
print "data are normal distributed"
return True
#对所有样本组进行正态性检验def NormalTest(list_groups):
for group in list_groups:
#正态性检验
status=check_normality(group1)
if status==False :
return False
#对所有样本组进行正态性检验 NormalTest(list_groups)