分层抽样（stratified sampling），又名层化抽出法，是统计学的一从统计总体（又称为「母体」）抽取样本方法。将抽样单位按某种特征或某种规则划分为不同的层，然后从不同的层中独立、随机地抽取样本。从而保证样本的结构与总体的结构比较相近，从而提高估计的精度。相对于没有经过分层的抽样调查，其数据会被称为「未分层抽样」（unstratified samples）。

在社会统计调查（statistical survey），当总体内的「子总体」（subpopulations）之间的差异较大，对每个子总体分别进行分层抽样调查，会令统计调查结果更为准确。子总体的分层必须为互斥，即每个总体的成员均只能属于一个分层。之后，可对每个子总体进行简单随机抽样或系统抽样。这样可令调查的代表性改善。相对于简体随机抽样采取的算术平均值，分层的抽样应采用加权平均值。

In statistics, stratified sampling is a method of sampling from a population.

In statistical surveys, when subpopulations within an overall population vary, it is advantageous to sample each subpopulation (stratum) independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. The strata should be mutually exclusive: every element in the population must be assigned to only one stratum. The strata should also be collectively exhaustive: no population element can be excluded. Then simple random sampling or systematic sampling is applied within each stratum. This often improves the representativeness of the sample by reducing sampling error. It can produce a weighted mean that has less variability than the arithmetic mean of a simple random sample of the population.