Question 1

Which of the following best describes the difference between a parameter and a statistic?

Accepted Answer

A parameter describes a population, while a statistic describes a sample.

Question 2

A researcher wants to study the average height of women in the United States. She randomly selects 200 women and finds their average height is 63.0 inches, while the true average for all women is 63.7 inches. What is the most likely explanation for the difference between these two values?

Accepted Answer

Sampling error due to random sample-to-sample variation.

Question 3

Which of the following is an example of continuous quantitative data?

Accepted Answer

The height of students measured in centimeters.

Question 4

A survey asks participants to select 'underweight', 'normal', or 'overweight'. What type of data is being collected?

Accepted Answer

Categorical (qualitative) data

Question 5

Which sampling method is most appropriate for studying a rare disease in a population?

Accepted Answer

Snowball sampling

Question 6

A researcher divides a population into subgroups based on gender and then randomly samples from each subgroup. What sampling method is being used?

Accepted Answer

Stratified sampling

Question 7

Which of the following is a source of nonsampling error caused by the respondent?

Accepted Answer

A respondent misreports their weight.

Question 8

A study reports $$p \u003c 0.001$$ for a 0.3% improvement in a medical outcome based on 2 million participants. Which kind of significance does it have, and which does it lack?

Accepted Answer

It has statistical significance but lacks practical significance.

Question 9

Which of the following best describes cluster sampling?

Accepted Answer

Divide the population into groups, select one or more groups, and study everyone in the chosen group(s).

Question 10

A researcher surveys every 12th name on a roster. What sampling method is this?

Accepted Answer

Systematic sampling

Question 11

Which of the following is a key reason why voluntary response sampling can produce bias?

Accepted Answer

People who choose to respond may differ systematically from those who do not.

Question 12

Which of the following is an example of discrete quantitative data that is infinite but countable?

Accepted Answer

The set of all positive integers.

Question 13

A researcher collects exact weights from participants and later categorizes them as 'underweight', 'normal', or 'overweight'. What is the advantage of this approach?

Accepted Answer

Exact numerical data can be categorized later, but categorical data cannot be made quantitative.

Question 14

Which of the following is NOT a stage in the statistical study process as described in the lecture?

Accepted Answer

Randomize

Question 15

A study uses convenience sampling to survey only students in the instructor's own class. What type of error is most likely to occur?

Accepted Answer

Nonrandom sampling error

Question 16

Which of the following statements about statistical and practical significance is TRUE?

Accepted Answer

A result can be statistically significant but not practically important.

Question 17

A researcher wants to ensure every member of the population has an equal chance of being selected, and every possible sample of size $$n is $$equally likely. What sampling method should be used?

Accepted Answer

Simple random sampling

Question 18

Which of the following is an example of a nonsampling error caused by the researcher?

Accepted Answer

A poorly worded survey question that leads respondents to a particular answer.

Question 19

A study finds that 52% of 100,000 couples have girls after using a new procedure, compared to 48% in the general population. The company advertises this as a significant improvement. What is the main concern with this claim?

Accepted Answer

The result is statistically significant but may lack practical significance.

Question 20

Which of the following best describes the difference between stratified and cluster sampling?

Accepted Answer

Stratified sampling selects some from every subgroup; cluster sampling selects everyone from selected groups.

Introductory Statistics: Foundations, Data Types, Sampling, and Study Design

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