Commonly Used Statistical Methods in This Journal

Abstract：Commonly used statistical methods: For categorical (nominal or ordinal) data, use chi-square test and Ridit analysis. For comparison of means of interval variables, use t-test, F-test, and rank sum test (suitable for interval variables that follow a skewed distribution). The application scope of the chi-square test: When the total sample size (n) > 40 and the expected frequency (T) > 5, use the basic formula of the chi-square test; if n > 40 and 1 < T < 5, use the corrected chi-square test; if n < 40 or T < 1, use the exact probability method of the chi-square test. T-test and F-test are suitable for interval variables that follow a normal distribution. The t-test is often used to compare the mean difference of paired design with the overall mean of 0 (e.g., before and after treatment comparison), and the comparison of means between two small samples in a group design. For the comparison of means between two large samples in a group design, use the U-test. The F-test is a comprehensive comparison (it can only detect whether there is a statistically significant difference in the means of two or more groups, but cannot determine which two groups differ significantly). For pairwise comparisons among multiple sample means, post-hoc tests (such as LSD, SNK, Dunnett's T3, Tamhane's T2 for unequal variances, etc.) can be used. The significance level is only set at P < 0.05 and P < 0.01. If statistical processing is performed using SPSS, SAS software, etc., specific P values can also be provided.