Skip to content

CFA Level I · CFA Level I Exam · Estimation and Hypothesis Testing

An analyst draws large random samples from a population that is clearly non-normal with a finite variance. According to the central limit theorem, the sampling distribution of the sample mean is most likely:

The sample mean is approximately normally distributed when the sample size is large. The central limit theorem holds for any population with finite variance, so the population's own shape does not carry over to the sampling distribution of the mean.

  1. Aapproximately normal when the sample size is largeCorrect
  2. Bidentical in shape to the population distribution
  3. Cnormal only when the population variance is unknown

Explanation

The central limit theorem says that for a large random sample from a population with finite variance, the sample mean is approximately normally distributed regardless of the population shape. The population shape is not preserved in the sampling distribution, and normality does not depend on the variance being unknown.

Did you get it right without looking?

One question tells you little. A timed set on Estimation and Hypothesis Testing shows your real accuracy, how long you take and where you lose marks.

Practise Estimation and Hypothesis TestingFree diagnostic mock

More Estimation and Hypothesis Testing questions