What is stratified sampling and when is it useful in MIPC?

• A. Only sampling from a single subgroup.
• B. Sampling randomly from entire population without strata.
• C. Dividing population into strata and sampling within each to ensure representative data across user types, data ranges, or regions.
• D. Stratified sampling is inappropriate for testing.

Answer: (C)

Stratified sampling partitions the population into subgroups that are similar within each group but different from other groups, then samples from every subgroup. This approach ensures that data reflect all important segments rather than being dominated by one large group, which helps reduce sampling error and improve the precision of overall estimates.

In MIPC, this is particularly useful because you often care about how outcomes vary across different user types, data ranges, or regions. By sampling within each stratum, you guarantee representation from each meaningful segment, making it possible to compare performance across segments and to obtain an overall estimate that truly reflects the whole population. You can sample proportionally to each stratum’s size or allocate a fixed number of samples per stratum to ensure smaller groups aren’t overlooked, depending on the goals and the variance within each group.

Avoid choosing just one subgroup or skipping the strata altogether, since those approaches can bias results and miss important differences. Stratified sampling is about using the structure of the population to get a clearer, more representative picture.