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Before embarking on data collection, researchers typically compute how manyindividual observations they should do. This is vital for doing studies withsufficient statistical power, and often a cornerstone in studypre-registrations and grant applications. For traditional statistical tests,one would typically determine an acceptable level of statistical power,(gu)estimate effect size, and then use both values to compute the requiredsample size. However, for analyses that identify subgroups, statistical poweris harder to establish. Once sample size reaches a sufficient threshold, effectsize is primarily determined by the number of measured features and theunderlying subgroup separation. As a consequence, a priory computations ofstatistical power are notoriously complex. In this tutorial, I will provide aroadmap to determining sample size and effect size for analyses that identifysubgroups. First, I introduce a procedure that allows researchers to formalisetheir expectations about effect sizes in their domain of choice, and use thisto compute the minimally required number of measured variables. Next, I outlinehow to establish the minimum sample size in subgroup analyses. Finally, I usesimulations to provide a reference table for the most popular subgroupanalyses: k-means, Ward agglomerative hierarchical clustering, c-means fuzzyclustering, latent class analysis, latent profile analysis, and Gaussianmixture modelling. The table shows the minimum numbers of observations perexpected subgroup (sample size) and features (measured variables) to achieveacceptable statistical power, and can be readily used in study design.

Tutorial: a priori estimation of sample size, effect size, and  statistical power for cluster analysis, latent class analysis, and  multivariate mixture models