What is the recommended minimum sample size for populations that are highly skewed or have outliers?

The recommended minimum sample size for populations that are highly skewed or have outliers is typically around 50. In statistical analysis, when dealing with non-normal distributions, skewness, or the presence of outliers, larger sample sizes can provide more reliable estimates and improve the robustness of statistical tests.

Using a sample size of 50 helps ensure that the central limit theorem applies more effectively, which states that as the sample size increases, the sampling distribution of the sample mean will tend to be normally distributed, regardless of the shape of the population distribution. This is important for valid inferential statistics because many statistical tests (e.g., t-tests) assume normality.

As sample sizes decrease, particularly below 30, the impacts of skewness and outliers become more pronounced, potentially leading to misleading results. Hence, a minimum sample size of 50 is advised in such scenarios to better capture the true characteristics of the population and to account for the effects of non-normality.