When constructing confidence intervals for population mean with population distribution that is not normal we based our calculations on the

Question

theorem.

In this case, to use the methods studied in this session, we need to make sure that the sample size is greater than
.

Answer 1

When constructing confidence intervals for the population mean when the population distribution is not normal, we typically rely on the Central Limit Theorem (CLT). The CLT states that, as the sample size increases, the sampling distribution of the sample mean approaches a normal distribution, regardless of the shape of the population distribution, provided the sample size is sufficiently large.

To ensure that the methods based on the Central Limit Theorem are valid, we often require that the sample size is greater than 30. This is a common guideline, suggesting that if the sample size is at least 30, the sampling distribution of the mean can be approximated by a normal distribution, which allows for the use of standard methods for constructing confidence intervals.