When calculating the necessary sample size for an interval estimate of a population proportion, which procedure is NOT recommended when p is unknown?

In determining the sample size required for an interval estimate of a population proportion, the aim is to ensure that the sample is sufficiently large to yield reliable results. When the true population proportion (p) is unknown, specific values can be used as estimates to maximize the sample size calculation's effectiveness.

Using .50 as an estimate is standard practice when p is unknown, as it provides a conservative estimate that maximizes the sample size, ensuring that the margin of error stays within acceptable bounds. This is based on the premise that the variance of the proportion is highest at p = 0.5, which inherently results in a larger sample requirement and thus a more thorough investigation of the population.

On the other hand, using an estimate of .95 is not recommended since it implies an extreme scenario where the proportion of the attribute being measured is very close to 1. This high estimate would lead to a misleading calculation, causing a sample size that is unnecessarily large relative to what is typically required for common population proportions.

In contrast, estimates such as .05 and .45 can be useful depending on the expected responses in the population. While they can be applied, they may not be as robust in maximizing the sample size needed compared to using .50. However,