Hi, all. We often use coefficients of variation (CV = standard error ÷ estimate) as a rough guideline for statistical reliability: CV < 0.15 is pretty trustworthy; CV > 0.30 is not to be trusted. But the CV value depends on how the estimate is expressed. Here's an example:

- The small, affluent city of Deephaven (Minnesota) has a poverty rate of 3.5%, with a MOE of 2.1 percentage points. Dividing the MOE by 1.645 yields a standard error of about 1.3. So the CV is around 0.37 (1.3 ÷ 3.5), which would be considered unreliable.
- If you flip that statistic around, you find that 96.5% of people in Deephaven are
*not*in poverty. The standard error doesn’t change, so now the CV is 0.01 (1.3 ÷ 96.5), which would be considered quite reliable. - So this is the exact same information (just expressed differently) but leads to very different judgments about reliability.

This is an extreme example, but it does get across the general idea. I'm curious how others have dealt with this, and whether there's a better way to give our audiences a quick and simple way to assess reliability.

Thanks for any perspective you all can offer.