Sampling distributions Coursera quiz Answers

Sampling distributions

1. 

What is the difference between descriptive and inferential statistics?

2. 

1(a), 2(b), 3(d), 4(c)

3. 

Which of the statement(s) is/are correct?

Both statements are incorrect.

4. 

How do you call the bias that can occur when not everybody from the population is included in the sampling frame?

Undercoverage

5. 

Imagine you want to know the length of the beard of every male student in America. You know that the population mean equals 2.2 millimeters and the population standard deviation equals 0.9 millimeters. What will be the mean (in millimeters) of the sampling distribution of the sample mean (i.e., if you take an infinite number of samples)?

2.2

6. 

What is the central limit theorem?

The central limit theorem says that the sampling distribution approximates a bell shape given that the sample is large enough.

7. 

Which of the following statement(s) is/are true?

The sampling distribution of the sample mean is the distribution of an infinite number of sample means (with a given sample size).

The larger the variability in the population distribution, the larger the variability in the sampling distribution of the sample mean.

8. 

This could be a population distribution, a data distribution or a sampling distribution.

9. 

You know that twenty percent of the people in Amsterdam describe themselves as Hipsters. You ask 400 respondents if they identify as a Hipster or not. What is the standard deviation of the sampling distribution of the sample proportion?

(2 decimals; use dot separator)

0.02

10. 

Which conclusion can you draw if a data distribution is very different from the corresponding population distribution (provided that the sample size is very large)?

The sample is biased and does not represent the population well.