Excluding zip codes with insufficient data (January 19, 2006). Category: Descriptive statistics

Someone asked about a study evaluating rates of children with tooth decay according to the zip code they live in. Some zip codes might have hundreds of children evaluated, and others may have only a handful. The question was how to determine when a zip code had so few evaluated children that it would make more sense not to report a rate at all, but instead label that zip code as having insufficient data.

There's no right or wrong answer here, and it may be worthwhile to experiment a bit.

You could use the width of the confidence interval as a guide and set a criteria like the following: we will not report data on any zip code where the width of the confidence interval was greater than plus/minus 20%. That will translate roughly into a sample size cutoff, though it may take a bit of work to figure out exactly what sample size will guarantee that all of your remaining confidence intervals will be narrower than plus/minus 20%. Since the rates also vary, you might find that for two zip codes with the same size, one would have a width slightly greater than plus/minus 20% and the other would be slightly narrower than plus/minus 20%. You should be able to use a bit of trial and error and find something that works reasonably well, or you can estimate the overall rate across all zip codes and find a sample size that would produce a width of plus/minus 20% when the rate was exactly equal to the overall rate.

This web page was written and was last modified on 09/24/2007.