The central limit theorem tells us:
A)In small samples, significance tests can't be trusted.
B)In small samples, the assumptions of parametric tests matter less.
C)If the sample is large enough we can assume homogeneity of variance.
D)If the sample is large enough, the sampling distribution of a parameter will be normal.

Question

Quizplus · Accepted Answer

The central limit theorem states that if the sample is large enough, the sampling distribution of a parameter will be normal. This means that as the sample size increases, the distribution of sample means will approach a normal distribution, regardless of the distribution of the population. This is why larger sample sizes are preferred in statistical analysis, as it allows for more reliable inference and the application of parametric tests with assumptions of normality.

The Central Limit Theorem Tells Us