- Thread starter daisystat
- Start date

Well, it depends on a few factors. If we are trying to detect small differences (i.e. effect size) in the percentages we will need larger samples relative to larger differences. It also depends on type 1 error, type 2 error, and power. Typically, we fix alpha to be .01, .05, or .10 and then observe sample sizes for various levels of power or vice versa. Generally, we like power to be 80% or higher. Power is the probability that we detect the effect given that the effect truly exists. All these calculations are driven by the context of the problem/research question.

Last edited:

I believe there are also differences depending on whether you are in the middle (e.g., p ~ 0.5) versus the extremes (e.g., p ~ 0.1 or 0.9).

The power of a proportions test decreases when you are in the middle, so you have to compensate by increasing the sample size.