How does sample size affect the distribution of data?

How does sample size affect the distribution of data?

The sample size can significantly impact the shape and characteristics of the data distribution. A larger sample size tends to provide a more accurate representation of the population, leading to a normal distribution due to the Central Limit Theorem.

What is the Central Limit Theorem?

The Central Limit Theorem states that the distribution of the sample means will approach a normal distribution as the sample size increases, regardless of the original distribution of the population.

Can you explain how a small sample size might misrepresent data?

A small sample size may lead to skewed results and increased variability, making it difficult to generalize findings to the larger population. This can result in misleading conclusions.

What is a good sample size for statistical analysis?

A good sample size depends on the study's goals, but generally, larger samples (e.g., 30 or more) are recommended to ensure reliability and validity of the results.