Question 1

The most important characteristic of a good sample is that it is free from what?&#10;A) Bias&#10;B) Random facts&#10;C) Random sampling

Accepted Answer

A good sample must be free from bias to accurately represent the population from which it is drawn. Bias in sampling can lead to skewed results that do not reflect the true characteristics of the population.

Question 2

Which is NOT true about a simple random sample?&#10;A) Put names on slips of paper and draw the number needed for the sample&#10;B) Use a table of random numbers to select participants&#10;C) Individuals in the population have an equal chance of being selected&#10;D) Divide the population into strata

Accepted Answer

A simple random sample does not involve dividing the population into strata; that method describes stratified sampling. In simple random sampling, every member of the population has an equal chance of being selected, and methods like drawing names or using random numbers are typical approaches.

Question 3

How is stratified random sampling superior to simple random sampling?&#10;A) Drawing participants at random from each stratum represents the same proportions in the population&#10;B) Increases the chance of sampling error&#10;C) Eliminates sampling error completely&#10;D) Use a table of random numbers to select participants

Accepted Answer

Stratified random sampling ensures that each subgroup (stratum) of the population is represented in the sample in the same proportions as they exist in the population, leading to more representative and accurate results compared to simple random sampling, which might not guarantee this representation.

Question 4

If a research is interested in how freshmen and seniors in college differ in their opinion on alcohol consumption and would like to draw samples separately from each group, what kind of sampling is the researcher using?

A) Simple random sampling
B) Stratified random sampling
C) Cluster random sampling

Accepted Answer

The researcher is using stratified random sampling because they are dividing the population into two distinct groups (freshmen and seniors) and then drawing samples from each group separately. This method ensures that each subgroup is represented in the sample.

Question 5

in a large scale sampling, multistage random sampling is used.

Accepted Answer

Multistage random sampling is often used in large-scale sampling to manage the complexity and cost by first dividing the population into clusters and then randomly selecting samples from those clusters in stages.

Question 6

What is NOT an advantage of cluster random sampling?&#10;A) There are fewer individuals for the researcher to contact&#10;B) It is useful for a large geographical area&#10;C) The degree of cooperation is likely to be higher&#10;D) The number of clusters instead of individuals to be treated as the sample size

Accepted Answer

Cluster random sampling involves selecting groups (or clusters) for the sample, not individuals. However, the disadvantage mentioned in D is not accurate. In cluster sampling, the entire cluster is typically surveyed, which means the sample size can actually be quite large depending on the number of individuals within each cluster. The statement in D seems to confuse cluster sampling with another method, such as stratified sampling, where the focus is on reducing the sample size by selecting specific strata.

Deck 6: Variations on Random Sampling