The central limit theorem implies select one: a. the distribution of a large population is approximately normal. b. the distribution of the average of a sample from a large population is approximately normal. c. the distribution of a large sample is approximately normal. d. the distribution of the average of a large sample is approximately normal.

d. the distribution of the average of a large sample is approximately normal.

Explanation:

The Central Limit Theorem refers to the convergence of sums of random variables for a normal distribution. Given its importance in theory and applications, this theorem is considered the most elementary probability theorem. According to the Central Limit Theorem, when the sample size is large enough, the mean distribution is an approximately normal distribution. The theorem applies regardless of the form of population distribution. This is a theorem of great importance to data science because many common statistical procedures require that the data be approximately normal.

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