Module 3 Part II.

The sampling of distribution of a mean is

• Distribution of individual data points in a sample
• Distribution of sample means from all possible random samples of a certain size taken from a population
• Distribution of population means from a single random sample
• Distribution of maximum values in a sample

The Central Limit Theorem tell us

• that the sampling distribution of the sample mean approaches a normal distribution as the sample size gets larger, regardless of the shape of the population distribution.

• it is only relevant for specific types of data.

• that the population distribution will always be normal.

• that it only applies to small sample sizes.

This is a visualization of

• sample mean

• sample standard deviation

• population mean

• population standard deviation

This is a visualization of

• sample mean

• sample standard deviation

• population mean

• population standard deviation

Recall that a lowercase n represents

• population size

• sample size

• number of data values

• number of elements

When working with sampling distribution of a mean, the population mean is also

• the population standard deviation

• the population median

• the sample mean

• the sample standard deviation

Which of these distributions are allowed to be have a sample size (n) of any size?

• uniform

• normal

• skewed (R)

• skewed (L)

• bimodal

Type a number.

The sample size (n) has to be greater than __ when our distribution is not normal.

The sampling distribution of a proportion is the distribution of

• Sample medians from all possible samples of the same size taken from a population
• Sample variances from all possible samples of the same size taken from a population
• Sample means from all possible samples of different sizes taken from a population
• Sample proportions from all possible samples of the same size taken from a population

A lowercase p represents

• population probabilities

• population proportions

• sample probabilities

• sample proportions