18.1 - Populations and Samples

Question 1

What is the definition of a simple random sample?

Answer 1

A type of procedure where every possible sample (of a given size) has an equal chance of being selected

Question 2

Is a simple random sample biased?

Answer 2

No

Question 3

What is an example of a simple random sample?

Answer 3

A random number generator (such as the randint function on a calculator)

Question 4

What is the common practice with replacing values?

Answer 4

It is not done

Question 5

Why?

Answer 5

Because it means that no piece of data could be selected more than once

Question 6

Why are simple random samples hard to execute in process?

Answer 6

Because very large data sets are generally chosen, and this means that getting a list of the entire population is a challenge, so the sampling procedure cannot be applied fully

Question 7

What is opportunity sampling?

Answer 7

Sampling based on availability and/or convenience

Question 8

Is an opportunity sample biased?

Answer 8

Yes

Question 9

What are the benefits of an opportunity sample?

Answer 9

Cheap and convenient

Question 10

What is systematic sampling?

Answer 10

Taking data values at regular intervals in the population, with the starting point chosen at random

Question 11

What is key about the list of data for systematic sampling?

Answer 11

It must be ordered in some way (e.g. alphabetically)

Question 12

Why is this key for systematic sampling?

Answer 12

Because it means that a good spread of data is collected, thus ensuring that the sampling method is random

Question 13

Why is systematic sampling less random than simple random sampling?

Answer 13

Because it is no longer independent

Question 14

What is the other drawback of systematic sampling?

Answer 14

It requires a list of the entire population

Question 15

What are the benefits of systematic sampling?

Answer 15

Avoids unwanted clustering of data and practically easier than a random number generator

Question 16

What is stratified sampling?

Answer 16

Splitting the population into groups based on factors relevant to the research, the random sampling from each group, in proportion to the size of that group

Question 17

What does this produce?

Answer 17

A representative sample over the factors identified

Question 18

What is the drawback, and what does this mean, of stratified sampling?

Answer 18

Needs an entire list of the population and additional information about each member, which is time-consuming and expensive

Question 19

What is the common alternative to stratified sampling?

Answer 19

Quota sampling

Question 20

What is quota sampling?

Answer 20

Splitting the population into groups based on factors relevant to the research, then opportunity sampling from each group until a required number of participants are found

Question 21

Is quota sampling biased?

Answer 21

Yes

Question 22

What is the benefit of using quota sampling?

Answer 22

It produces a representative sample over the factors identified

Question 23

What is cluster sampling?

Answer 23

Splitting the population into clusters based on convenience, then randomly choosing some clusters to study further

Question 24

What type of sample does cluster sampling produce?

Answer 24

An unrepresentative one

Question 25

What is the main benefit of cluster sampling?

Answer 25

Cheaper and easier than other random methods