According to the Central Limit Theorem, The traditional sample size that separates a large sample size from a small sample size is one that is greater than 100

30
500
20

30

Well, the Central Limit Theorem is like the Goldilocks of statistics. It says that if you have a large enough sample size, it can give you a "just right" approximation of the population mean. So, it's not so much about a specific magic number, but more about having a sample size that's large enough to make your estimates pretty darn accurate. So, while there's no definitive answer, I'd go with the options 100, 30, or 500, and leave the number 20 to be the pint-sized portion of porridge that just doesn't quite cut it.

According to the Central Limit Theorem, the traditional sample size that separates a large sample size from a small sample size is considered to be greater than 30.

To determine the traditional sample size that separates a large sample size from a small sample size according to the Central Limit Theorem, we need to understand the concept of the Central Limit Theorem and its implications.

The Central Limit Theorem 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. This means that even if the population is not normally distributed, the distribution of sample means will become approximately normal as the sample size gets larger.

However, there is no specific sample size mentioned in the Central Limit Theorem that differentiates between large and small sample sizes. The specific sample size to consider as "large" or "small" depends on the context and the desired level of accuracy needed for the statistical analysis.

In general, sample sizes beyond 30 are often considered large enough to approximate a normal distribution. This rule of thumb is commonly used because for most practical purposes, a sample size of around 30 is sufficient to provide reliable estimates.

Therefore, based on the options provided, a traditional sample size that can be considered "large enough" according to the Central Limit Theorem would be:

- Greater than 30

While the options suggested sample sizes of 100, 500, and 20, the sample size of 30 is the closest option to the commonly used threshold for a "large enough" sample size. However, it is important to note that the optimal sample size depends on various factors such as the population distribution, the desired level of precision, and the specific statistical analysis being performed.