**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.