From ChatGPT: Give a simple explanation of the concept of "post-stratification" in sampling

Post-stratification is a sampling technique used in survey research to improve the precision of estimates by adjusting for known population characteristics. In simple terms, it involves dividing the population into different subgroups or "strata" based on certain characteristics (such as age, gender, income, or education). After the initial random sampling, researchers analyze the data within each stratum separately.

The key idea is that certain subgroups may be underrepresented or overrepresented in the initial sample, and post-stratification helps correct for this. By examining each stratum individually, researchers can ensure that the final results are more accurate and reflective of the overall population. This technique is particularly useful when there are significant variations in the characteristics of the population being studied.

After analyzing the data within each stratum, you need to recombine the results to obtain an overall estimate for the entire population. The general process involves weighting the results from each stratum based on the proportion of the population it represents.