Question

A large population has skewed data with a mean of 70 and a standard deviation of 6. Samples of size 100 are taken, and the distribution of the means of these samples is analyzed. a) Will the distribution of the means be closer to a normal distribution than the distribution of the population?

b) Will the mean of the means of the samples remain close to 70?
c) Will the distribution of the means have a smaller standard deviation?
d) What is that standard deviation?
a. Yes.
b. Yes.
c. Yes.
d. 0.6.

Answer 1

a) Yes

b) Yes

c) Yes

d) 0.6

Explanation:

Central Limit Theorem

The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean
`\mu` and standard deviation
`\sigma`, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
`\mu` and standard deviation
`s = (\sigma)/(√(n))`.

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

A large population has skewed data with a mean of 70 and a standard deviation of 6.

This means that
`\mu = 70, \sigma = 6`

Samples of size 100

This means that
`n = 100`

a) Will the distribution of the means be closer to a normal distribution than the distribution of the population?

According to the Central Limit Theorem, yes.

b) Will the mean of the means of the samples remain close to 70?

According to the Central Limit Theorem, yes.

c) Will the distribution of the means have a smaller standard deviation?

According to the Central Limit Theorem, the standard deviation of the population is divided by the sample size, so yes.

d) What is that standard deviation?

`s = (\sigma)/(√(n)) = (6)/(√(100)) = (6)/(10) = 0.6`

So 0.6.