[Visitor (112.21.*.*)]answers [Chinese ]  Time :20210908  Nonparametric test As mentioned earlier, data analysis can be performed using nonparametric testing for data that does not conform to the normal distribution. Here, data that does not conform to the normal distribution can be divided into two kinds: 1, highmeasurement data that do not conform to the normal distribution (fixeddistance data and highsequencing data); According to the above two data types, nonparametric testing mainly includes the following three aspects: Test the distribution pattern of the sample The distribution pattern of highmeasurement data series is tested for single variables by examining the difference between the distribution of data series and the standard distribution pattern. If there is no significant difference between the current data series and the standard distribution pattern, the current sequence is considered to satisfy the distribution pattern. Common test techniques for determining the distribution pattern of single sample data are: single sample KS test, single sample run test, two distribution test, and square test. The significance test of distribution pattern difference For highmeasurement data series that do not conform to the normal distribution, the common differential significance test methods are: 1, the difference significance test of the two independent samples; The differential significance test of low measurement data For class data that do not conform to the normal distribution or lowquality sequencing data, the test method is to calculate the frequency of intersections by using crosssectional branch, to implement the square test by the square distance, and to analyze whether there are significant differences between different categories of data based on frequency number and data distribution pattern. For the comparative test of class data, it is also called the independence test. The significance test of distribution pattern difference The distribution pattern test has been described earlier, and the square test of lowmeasurement data will be described in the next article. The following focuses on the distribution pattern significance test method for highmeasurement data for nonnormal distribution.
Nonparametric testing of two associated samples For two associated samples that do not meet the normal distribution, if they are analyzed for significant differences, their differences can not be compared by mean, usually by comparing their distribution patterns. The three requirements of the data series: 1, the sample data comes from different perspectives of the same population, or multiple measurements of the same sample; 
