PORTAGE LEARNING CNSL 503 MODULE 3: SAMPLING, PROBABILITY, AND
HYPOTHESIS TESTING QUESTIONS AND ANSWERS WITH COMPLETE SOLUTIONS |
NEW UPDATE 2026!!!
Question 1
Which of the following best describes the difference between a statistic and a parameter?
A) A statistic describes a population, while a parameter describes a sample.
B) A statistic is a numerical value from a sample; a parameter is a numerical value from a
population.
C) Statistics are always qualitative, while parameters are always quantitative.
D) Parameters are calculated using inferential methods, while statistics are purely descriptive.
E) There is no functional difference between the two terms in research.
Correct Answer: B) A statistic is a numerical value from a sample; a parameter is a
numerical value from a population.
Rationale: In research, we distinguish between data derived from a subset (the sample) and
the entire group (the population). Any descriptive result generated from sample data is
called a statistic, whereas the corresponding value for the entire population is a parameter.
Question 2
What is a "representative sample"?
A) A sample where participants are chosen based on availability.
B) A sample that possesses the same general characteristics as the population.
C) A sample that is significantly larger than the population.
D) A sample composed only of outliers.
E) A sample that excludes all subgroups within a population.
Correct Answer: B) A sample that possesses the same general characteristics as the
population.
Rationale: The goal of sampling is to mirror the population as closely as possible. A
representative sample allows researchers to make accurate inferences about the population
because the sample behaves similarly to the whole.
Question 3
Which term is used to describe a sample that is not representative of the population, often due to
researcher or collection methods?
A) Stratified
B) Systematic
C) Biased
D) Random
E) Proportional
Correct Answer: C) Biased
Rationale: A biased sample does not accurately reflect the population. This lack of
representation usually stems from flaws in how the data was collected or how the sample
was selected, preventing accurate inferences.
, 2
Question 4
In random sampling, what is the significance of "independence"?
B) It means the researcher can choose whoever they want.
B) Selecting one member does not change the likelihood of selecting another.
C) It means the sample is not part of the population.
D) It indicates that the sample size must be equal to 1.
E) It ensures that the results will be 100% accurate.
Correct Answer: B) Selecting one member does not change the likelihood of selecting
another.
Rationale: Independence is a core requirement of random sampling. If the selection of one
person influences the selection of another, the sample is no longer truly random, which can
compromise the reliability of the statistical results.
Question 5
Why is "Simple Random Sampling" often considered non-feasible for large populations?
A) It requires the population to be divided into strata first.
B) It relies on convenience rather than chance.
C) It requires an exhaustive list of every member and equal access to them all.
D) It is only used for qualitative research.
E) It does not result in a representative sample.
Correct Answer: C) It requires an exhaustive list of every member and equal access to them
all.
Rationale: Simple Random Sampling requires that every single individual in a population
has an equal chance of selection. In massive populations (like the citizens of a country),
identifying and reaching every person to give them an equal chance is logistically
impossible.
Question 6
Which sampling method involves dividing the population into subgroups called "strata" to
compare outcomes?
A) Cluster sampling
B) Systematic sampling
C) Stratified sampling
D) Convenience sampling
E) Simple random sampling
Correct Answer: C) Stratified sampling
Rationale: Stratified sampling is specifically used when a researcher wants to ensure that
specific subgroups (strata) are represented in the study, allowing for comparisons between
those different groups.
, 3
Question 7
A researcher wants to ensure the sample proportions of "Male" and "Female" match the exact
proportions found in the national population. Which method should be used?
A) Non-proportional stratified sampling
B) Proportional stratified sampling
C) Convenience sampling
D) Cluster sampling
E) Systematic sampling
Correct Answer: B) Proportional stratified sampling
Rationale: Proportional stratified sampling creates a sample that mirrors the population's
ratios. If a population is 60% female, the sample will also be 60% female to maintain
proportional stability.
Question 8
When would a researcher prefer "Non-proportional stratified sampling"?
A) When they want the sample to look exactly like the population.
B) When they want to gather equal sample sizes from each group regardless of their population
ratio.
C) When they only want to study the majority group.
D) When they have no access to population data.
E) When they want to eliminate sampling error entirely.
Correct Answer: B) When they want to gather equal sample sizes from each group
regardless of their population ratio.
Rationale: If a researcher needs to compare a very small minority group to a very large
majority group, they may use non-proportional sampling to ensure they have enough data
from the minority group to make a valid comparison.
Question 9
Which sampling method is most cost-effective and efficient when participants are geographically
spread out?
A) Systematic sampling
B) Simple random sampling
C) Cluster sampling
D) Convenience sampling
E) Non-proportional sampling
Correct Answer: C) Cluster sampling
Rationale: Cluster sampling divides the population into geographic subgroups (clusters) and
then randomly selects whole clusters to study. This saves time and money because the
researcher only has to visit a few specific locations.
HYPOTHESIS TESTING QUESTIONS AND ANSWERS WITH COMPLETE SOLUTIONS |
NEW UPDATE 2026!!!
Question 1
Which of the following best describes the difference between a statistic and a parameter?
A) A statistic describes a population, while a parameter describes a sample.
B) A statistic is a numerical value from a sample; a parameter is a numerical value from a
population.
C) Statistics are always qualitative, while parameters are always quantitative.
D) Parameters are calculated using inferential methods, while statistics are purely descriptive.
E) There is no functional difference between the two terms in research.
Correct Answer: B) A statistic is a numerical value from a sample; a parameter is a
numerical value from a population.
Rationale: In research, we distinguish between data derived from a subset (the sample) and
the entire group (the population). Any descriptive result generated from sample data is
called a statistic, whereas the corresponding value for the entire population is a parameter.
Question 2
What is a "representative sample"?
A) A sample where participants are chosen based on availability.
B) A sample that possesses the same general characteristics as the population.
C) A sample that is significantly larger than the population.
D) A sample composed only of outliers.
E) A sample that excludes all subgroups within a population.
Correct Answer: B) A sample that possesses the same general characteristics as the
population.
Rationale: The goal of sampling is to mirror the population as closely as possible. A
representative sample allows researchers to make accurate inferences about the population
because the sample behaves similarly to the whole.
Question 3
Which term is used to describe a sample that is not representative of the population, often due to
researcher or collection methods?
A) Stratified
B) Systematic
C) Biased
D) Random
E) Proportional
Correct Answer: C) Biased
Rationale: A biased sample does not accurately reflect the population. This lack of
representation usually stems from flaws in how the data was collected or how the sample
was selected, preventing accurate inferences.
, 2
Question 4
In random sampling, what is the significance of "independence"?
B) It means the researcher can choose whoever they want.
B) Selecting one member does not change the likelihood of selecting another.
C) It means the sample is not part of the population.
D) It indicates that the sample size must be equal to 1.
E) It ensures that the results will be 100% accurate.
Correct Answer: B) Selecting one member does not change the likelihood of selecting
another.
Rationale: Independence is a core requirement of random sampling. If the selection of one
person influences the selection of another, the sample is no longer truly random, which can
compromise the reliability of the statistical results.
Question 5
Why is "Simple Random Sampling" often considered non-feasible for large populations?
A) It requires the population to be divided into strata first.
B) It relies on convenience rather than chance.
C) It requires an exhaustive list of every member and equal access to them all.
D) It is only used for qualitative research.
E) It does not result in a representative sample.
Correct Answer: C) It requires an exhaustive list of every member and equal access to them
all.
Rationale: Simple Random Sampling requires that every single individual in a population
has an equal chance of selection. In massive populations (like the citizens of a country),
identifying and reaching every person to give them an equal chance is logistically
impossible.
Question 6
Which sampling method involves dividing the population into subgroups called "strata" to
compare outcomes?
A) Cluster sampling
B) Systematic sampling
C) Stratified sampling
D) Convenience sampling
E) Simple random sampling
Correct Answer: C) Stratified sampling
Rationale: Stratified sampling is specifically used when a researcher wants to ensure that
specific subgroups (strata) are represented in the study, allowing for comparisons between
those different groups.
, 3
Question 7
A researcher wants to ensure the sample proportions of "Male" and "Female" match the exact
proportions found in the national population. Which method should be used?
A) Non-proportional stratified sampling
B) Proportional stratified sampling
C) Convenience sampling
D) Cluster sampling
E) Systematic sampling
Correct Answer: B) Proportional stratified sampling
Rationale: Proportional stratified sampling creates a sample that mirrors the population's
ratios. If a population is 60% female, the sample will also be 60% female to maintain
proportional stability.
Question 8
When would a researcher prefer "Non-proportional stratified sampling"?
A) When they want the sample to look exactly like the population.
B) When they want to gather equal sample sizes from each group regardless of their population
ratio.
C) When they only want to study the majority group.
D) When they have no access to population data.
E) When they want to eliminate sampling error entirely.
Correct Answer: B) When they want to gather equal sample sizes from each group
regardless of their population ratio.
Rationale: If a researcher needs to compare a very small minority group to a very large
majority group, they may use non-proportional sampling to ensure they have enough data
from the minority group to make a valid comparison.
Question 9
Which sampling method is most cost-effective and efficient when participants are geographically
spread out?
A) Systematic sampling
B) Simple random sampling
C) Cluster sampling
D) Convenience sampling
E) Non-proportional sampling
Correct Answer: C) Cluster sampling
Rationale: Cluster sampling divides the population into geographic subgroups (clusters) and
then randomly selects whole clusters to study. This saves time and money because the
researcher only has to visit a few specific locations.
