Quiz on Sampling Distribution: Concepts and Techniques (V2)

The ​ (a)   says that the ​ (b)   of the ​ (c)   will always be ​ (d)   , as long as the ​ (e)   is large enough.

• central limit theorem
• sampling distribution
• mean
• normally distributed
• sample size
• wisdom of crowds
• uniformly distributed
• standard deviation

A ​ (a)   distribution is a ​ (b)   distribution of a ​ (c)   obtained from a larger number of ​ (d)   drawn from a specific ​ (e)   .

• sampling
• probability
• statistic
• random samples
• population
• normal
• parameter
• convenience samples

What is the standard deviation of the distribution shown in this image?

Mathematical Equivalence

ON

What happens to the shape of a sampling distribution of sample means as n increases?

• It becomes narrower and bimodal.
• It becomes narrower and more normal.
• It becomes wider and skewed right. 
• It becomes wider and more normal. 

A ​ (a)   is taken from a ​ (b)   in order to estimate a ​ (c)   . From the resulting data, a ​ (d)   is calculated (such as an ​ (e)   ) and that value is used as an unbiased estimate of the parameter.

• random sample
• population
• parameter
• statistic
• average
• convenience sample
• nominal value
• histogram

Click on the symbols that relate to averages.

There are two distribution graphs shown in the attached image; one graph is a sampling distribution and one is a population distribution. Click on the one that represents the population distribution.

Which of the following statements about the effect of sample size is true?

• Sample size affects the skew of the sampling distribution of the mean when the parent population is normal.

• Regardless of the shape of the parent population, the sampling distribution of the mean approaches a normal distribution as sample size increases.

Each one of these distributions was repeatedly sampled from to produce a sampling distribution of means. Choose the one(s) whose sampling distributions were normally shaped.

Answer explanation

When you repeatedly sample from ANY distribution, you obtain something normally shaped. EVERY one of these produced a normal distribution.

Click on all of the distributions that could be sampling distributions.