无母数统计分析（英语：nonparametric statistics），或称非参数统计学，统计学的分支，适用于母群体分布情况未明、小样本、母群体分布不为常态也不易转换为常态。特点在于尽量减少或不修改其创建之模型，较具稳健特性；在样本数不大时，计算过程较简单。

无母数统计推论时所使用的统计量的抽样分配通常与母体分配无关，不必推论其中位数、适合度、独立性、随机性，更广义的说，无母数统计又称为「不受分配限制统计法」（distribution free）。无母数统计缺乏一般之机率表。检定时是以等级（Rank）为主要统计量。

Nonparametric statistics are statistics not based on parameterized families of probability distributions. They include both descriptive and inferential statistics. The typical parameters are the mean, variance, etc. Unlike parametric statistics, nonparametric statistics make no assumptions about the probability distributions of the variables being assessed. The difference between parametric models and non-parametric models is that the former has a fixed number of parameters, while the latter grows the number of parameters with the amount of training data. Note that the non-parametric model does not have any parameters: parameters are determined by the training data, not the model.