写在前面：
很多人发表论文，材料与方法里，统计方法如何写不知所措，今天松哥写一个较为全面的模板，大家留存备用吧！
1. 统计软件：
统计分析采用SPSS19.0（SAS9.4/Stata14.0/etc）统计分析软件。
2.统计描述：
2.1计量资料符合正态分布采用均数±标准差表示；
2.2计量资料不符合正态分布采用中位数（四分位数间距）表示；
2.3计数资料采用率或构成比表示；
3.假设检验：
3.1符合正态分布与方差齐性的两组间计量资料比较采用两独立样本t检验
3.2不符合正态分布与方差齐性的两组计量资料比较采用非参数Mann -Whitney U检验；
3.3配对设计计量资料比较，差值符合正态分布采用配对t检验；差值不符合正态分布采用非参数Willcoxon秩和检验（/Sign检验）；
3.4多组间计量资料比较符合条件采用单因素设计方差分析，组间两两比较采用LSD法（SNK法/Bonferroni法/Duncan法。。。）；多组间计量资料比较不符合应用条件采用Kruskal-Wallis H法；
3.5配伍组设计计量资料比较符合条件采用随机区组设计方差分析；不符合条件采用非参数Friedman检验。
3.6等级资料组间比较采用非参数秩和检验；
3.7成组四格表计数资料符合条件采用Pearson卡方检验，不符合条件采用Pearson连续校正卡方检验或Fisher确切概率法；
3.8成组设计R×C表符合条件采用Pearson卡方检验，不符合条件采用Monte Carlo近似确切概率法。
4.相关
4.1：双变量正态分布资料采用Pearson相关系数；双变量非正态分布或等级资料采用Spearman相关系数。
4.2：两组资料间相关采用典型相关分析（canonical correlation analysis）。
5.回归：
5.1线性回归：因变量为计量资料的影响因素分析采用多元线性回归；
5.2：Logistic回归：二分类因变量影响因素分析采用二元Logistic回归（Binary Logistic）；有序因变量影响因素分析采用有序逻辑回归（Oridinal Logistic）；无序多分类计数资料因变量影响因素分析采用多项Logistic回归（Multinominal Logistic）。
6.聚类分析：
研究样品聚类采用系统聚类（hierachical cluster）；研究变量间聚类采用快速聚类（K-means cluster）。（如果数据既有计量又有计数资料）样本聚类采用两步聚类（Twosteps cluster）。
7.其他
方法不可一文盖之，其他大家发挥吧！
8.检验水准：P<0.05或P<0.01为差异有统计学意义。
来篇英文范文吧！
Summary statistics for normally distributed quantitativevariables were expressed as means and standard deviations. For non-normally distributedvariables, we used median and IQR; categorical data were summarized by ratiosand percentages. Differences in means for continuous variables were comparedusing Student’s t-test (two groups) or analysis of variance (multiplegroups), and differences in proportions were tested by x2 test. Cox proportional hazards models were used toanalyze the association of serum Gd-IgA1 levels and the primary outcome.Gd-IgA1 was highly skewed to the right in this group patients and natural logtransformation was used. Serum Gd-IgA1 was first analyzed as a continuousvariable with HRs calculated per s.d. increment of natural log–transformedGd-IgA1, and the Gd-IgA1 quartile as a categorical variable, with the lowestquartile defined as the reference group. The relationship between Gd-IgA1 andrisk of end point was examined in unadjusted and multivariable-adjusted Coxmodels. Proportional hazards assumptions were verified by testing theinteraction of survival time and lnGd-IgA1 and quartiles of Gd-IgA1 (P¼0.23 and P¼0.17,respectively), and by inspecting parallelism of estimated hazard functions. A two-sided P-value 0.05 was consideredstatistically significant. All statistical tests were performed using SPSSversion 16.0 (SPSS, Chicago, IL).
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