CNSL503 / CNSL 503 Module 6: (Latest Update 2026) Statistics
| 200 Practice Questions | Multi-Choice with Answer Key &
Explanations
1. Statistics is used to prove claims with absolute certainty.
A) True
B) False
C) Only in experimental research
D) Only when sample size is large
Answer: B) False
Explanation: Statistics does not "prove" claims with absolute certainty. Instead, it provides evidence to
support or refute hypotheses by calculating probabilities. Conclusions are always probabilistic, not
deterministic. In null hypothesis significance testing, we either reject or fail to reject the null
hypothesis—we never "prove" it.
2. What are variables?
A) Only numerical measurements in research
B) Categories assigned to participants
C) Measurable characteristics that can vary in value
D) The results of hypothesis testing
Answer: C) Measurable characteristics that can vary in value
Explanation: A variable is any characteristic, attribute, or quantity that can be measured and can assume
different values across individuals or over time. Examples include age, height, test scores, gender, and
satisfaction ratings.
3. What type of scale is used when labeling or using categories such as blood type, college majors, or
room numbers?
A) Ordinal
B) Interval
C) Ratio
,D) Nominal
Answer: D) Nominal
Explanation: Nominal scales are used for qualitative data where values are assigned to categories as
labels or names with no inherent order or ranking. Blood type, college majors, and room numbers are all
examples of nominal variables.
4. Which of the following are examples of ordinal variables? (Select all that apply)
A) Shirt sizes (Small, Medium, Large)
B) College major
C) Places in a marathon (1st, 2nd, 3rd)
D) Temperature in Celsius
Answer: A) Shirt sizes; C) Places in a marathon
Explanation: Ordinal scales are used for qualitative variables that can be ranked or ordered, but the
intervals between ranks are not necessarily equal. Shirt sizes indicate order, and places in a marathon
indicate rank order.
5. When a variable can contain negative values, what type of scale is most appropriate?
A) Nominal
B) Ordinal
C) Interval
D) Ratio
Answer: C) Interval
Explanation: An interval scale is appropriate because variables containing negative values do not have
an absolute zero. This is quantitative data where differences between values are meaningful, but ratios
are not.
6. When is it most appropriate to use a ratio scale?
A) When a variable has an absolute zero
B) When a variable is categorical
C) When a variable has no zero point
D) When a variable can be ranked
,Answer: A) When a variable has an absolute zero
Explanation: A ratio scale is used when a variable has an absolute zero, allowing for meaningful ratios
and statements like "twice as much." Examples include height, weight, and age.
7. True or False: It is generally feasible to collect data from every member of a population.
A) True
B) False
Answer: B) False
Explanation: Unless the target population is very small, it is usually not practical or sensible to attempt
to reach every member within a population. This is why sampling is used.
8. Which of the following best describes a representative sample?
A) A sample that includes every member of the population
B) A sample that is composed of members that generally possess the same characteristics as those of
the population
C) A sample that is selected based on convenience
D) A sample that includes only volunteers
Answer: B) A sample that is composed of members that generally possess the same characteristics as
those of the population
Explanation: A representative sample is one where the members share key characteristics with the
population, allowing for accurate generalizations to be made.
9. What is random sampling?
A) Selecting participants based on their availability
B) A sampling method where every member of the population has an equal chance of being selected
C) Selecting participants based on specific characteristics
D) Dividing the population into subgroups and sampling from each
Answer: B) A sampling method where every member of the population has an equal chance of being
selected
Explanation: Random sampling increases the reliability of study results. It is a form of probability
sampling where each member is independent of the others.
, 10. What is simple random sampling?
A) Selecting participants based on a rule (e.g., every 10th person)
B) Dividing a population into clusters and randomly selecting clusters
C) Each individual in the population has an equal chance of being selected for the sample
D) Selecting individuals because they happen to be at a certain place
Answer: C) Each individual in the population has an equal chance of being selected for the sample
Explanation: Simple random sampling gives each member an equal chance of selection. Examples
include drawing names from a hat.
11. What is a limitation of simple random sampling?
A) It is not practical to use for large populations
B) It is always biased
C) It cannot be used for qualitative data
D) It requires too much time even for small populations
Answer: A) It is not practical to use for large populations
Explanation: While simple random sampling relies on chance to create a representative sample, it is not
practical for large populations due to logistical and administrative constraints.
12. What is stratified sampling?
A) A population is divided into subgroups called strata, and data is randomly gathered from these
subgroups
B) Every 100th person is selected from a list
C) Individuals are selected because they are easily accessible
D) The population is divided into clusters and entire clusters are selected
Answer: A) A population is divided into subgroups called strata, and data is randomly gathered from
these subgroups
Explanation: Stratified sampling is used when a researcher wants to compare outcomes for different
subgroups (strata) such as race, gender, or socioeconomic status.
| 200 Practice Questions | Multi-Choice with Answer Key &
Explanations
1. Statistics is used to prove claims with absolute certainty.
A) True
B) False
C) Only in experimental research
D) Only when sample size is large
Answer: B) False
Explanation: Statistics does not "prove" claims with absolute certainty. Instead, it provides evidence to
support or refute hypotheses by calculating probabilities. Conclusions are always probabilistic, not
deterministic. In null hypothesis significance testing, we either reject or fail to reject the null
hypothesis—we never "prove" it.
2. What are variables?
A) Only numerical measurements in research
B) Categories assigned to participants
C) Measurable characteristics that can vary in value
D) The results of hypothesis testing
Answer: C) Measurable characteristics that can vary in value
Explanation: A variable is any characteristic, attribute, or quantity that can be measured and can assume
different values across individuals or over time. Examples include age, height, test scores, gender, and
satisfaction ratings.
3. What type of scale is used when labeling or using categories such as blood type, college majors, or
room numbers?
A) Ordinal
B) Interval
C) Ratio
,D) Nominal
Answer: D) Nominal
Explanation: Nominal scales are used for qualitative data where values are assigned to categories as
labels or names with no inherent order or ranking. Blood type, college majors, and room numbers are all
examples of nominal variables.
4. Which of the following are examples of ordinal variables? (Select all that apply)
A) Shirt sizes (Small, Medium, Large)
B) College major
C) Places in a marathon (1st, 2nd, 3rd)
D) Temperature in Celsius
Answer: A) Shirt sizes; C) Places in a marathon
Explanation: Ordinal scales are used for qualitative variables that can be ranked or ordered, but the
intervals between ranks are not necessarily equal. Shirt sizes indicate order, and places in a marathon
indicate rank order.
5. When a variable can contain negative values, what type of scale is most appropriate?
A) Nominal
B) Ordinal
C) Interval
D) Ratio
Answer: C) Interval
Explanation: An interval scale is appropriate because variables containing negative values do not have
an absolute zero. This is quantitative data where differences between values are meaningful, but ratios
are not.
6. When is it most appropriate to use a ratio scale?
A) When a variable has an absolute zero
B) When a variable is categorical
C) When a variable has no zero point
D) When a variable can be ranked
,Answer: A) When a variable has an absolute zero
Explanation: A ratio scale is used when a variable has an absolute zero, allowing for meaningful ratios
and statements like "twice as much." Examples include height, weight, and age.
7. True or False: It is generally feasible to collect data from every member of a population.
A) True
B) False
Answer: B) False
Explanation: Unless the target population is very small, it is usually not practical or sensible to attempt
to reach every member within a population. This is why sampling is used.
8. Which of the following best describes a representative sample?
A) A sample that includes every member of the population
B) A sample that is composed of members that generally possess the same characteristics as those of
the population
C) A sample that is selected based on convenience
D) A sample that includes only volunteers
Answer: B) A sample that is composed of members that generally possess the same characteristics as
those of the population
Explanation: A representative sample is one where the members share key characteristics with the
population, allowing for accurate generalizations to be made.
9. What is random sampling?
A) Selecting participants based on their availability
B) A sampling method where every member of the population has an equal chance of being selected
C) Selecting participants based on specific characteristics
D) Dividing the population into subgroups and sampling from each
Answer: B) A sampling method where every member of the population has an equal chance of being
selected
Explanation: Random sampling increases the reliability of study results. It is a form of probability
sampling where each member is independent of the others.
, 10. What is simple random sampling?
A) Selecting participants based on a rule (e.g., every 10th person)
B) Dividing a population into clusters and randomly selecting clusters
C) Each individual in the population has an equal chance of being selected for the sample
D) Selecting individuals because they happen to be at a certain place
Answer: C) Each individual in the population has an equal chance of being selected for the sample
Explanation: Simple random sampling gives each member an equal chance of selection. Examples
include drawing names from a hat.
11. What is a limitation of simple random sampling?
A) It is not practical to use for large populations
B) It is always biased
C) It cannot be used for qualitative data
D) It requires too much time even for small populations
Answer: A) It is not practical to use for large populations
Explanation: While simple random sampling relies on chance to create a representative sample, it is not
practical for large populations due to logistical and administrative constraints.
12. What is stratified sampling?
A) A population is divided into subgroups called strata, and data is randomly gathered from these
subgroups
B) Every 100th person is selected from a list
C) Individuals are selected because they are easily accessible
D) The population is divided into clusters and entire clusters are selected
Answer: A) A population is divided into subgroups called strata, and data is randomly gathered from
these subgroups
Explanation: Stratified sampling is used when a researcher wants to compare outcomes for different
subgroups (strata) such as race, gender, or socioeconomic status.
