Stratification is a technique developed for survey sampling in which a population is partitioned into subgroups (i.e., stratified) and each group (i.e., stratum) is sampled independently. If the subgroups are more homogeneous (i.e., lower variance) than the population as a whole, this can reduce variance in the estimate of a quantity of interest at the population level.
Poststratification is a technique for adjusting a non-representative sample (i.e., a convenience sample or other observational data) for which there are demographic predictors characterizing the strata. It is carried out after a model is fit to the observed data, hence the name poststratification (Little 1993). Poststratification can be fruitfully combined with regression modeling (or more general parametric modeling), which provides estimates based on combinations of predictors (or general parameters) rather than raw counts in each stratum. Multilevel modeling is useful in determining how much partial pooling to apply in the regressions, leading to the popularity of the combination of multilevel regression and poststratification (MRP) (Park, Gelman, and Bafumi 2004).
Little, Roderick JA. 1993. “Post-Stratification: A Modeler’s Perspective.” Journal of the American Statistical Association 88 (423): 1001–12.
Park, David K, Andrew Gelman, and Joseph Bafumi. 2004. “Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls.” Political Analysis 12 (4): 375–85.