BUS5112 Quantitative Research Methods Unit 8 Graded Quiz — 25
Q&A Verified Answers 2025
Course:
BUS5112 Quantitative Research Methods — University of the People (UoPeople)
Level:
MBA
Year:
2025/2026
Format:
Graded Quiz Solutions — 25 Q&A with Verified Answers
BUS5112 Unit 8 Graded Quiz — Research Reporting and Quality
25 Questions with Verified Answers | Score: 96/100
Question 1:
When distinguishing between statistical significance and practical significance in business
research, which statement is the most accurate?
A) Statistical significance is a subjective measure of business impact, while practical significance
is determined strictly by the p-value.
B) Statistical significance indicates that an effect likely exists, while practical significance
evaluates whether the magnitude of that effect is meaningful for real-world business decisions.
C) Practical significance guarantees that a research finding will generate higher revenue,
rendering statistical significance irrelevant.
D) They are identical concepts; any statistically significant finding is automatically practically
significant for a business.
Answer: B
Explanation:
Statistical significance (usually denoted by a p-value) simply tells us whether we can reject the
null hypothesis, meaning an effect likely exists and is not due to chance. Practical significance,
often measured by effect size, tells us if that effect is large enough to care about in a practical,
real-world business context. A finding can be statistically significant but practically meaningless.
, Question 2:
In quantitative reporting, what does "effect size" measure?
A) The probability that the researcher committed a Type I error during hypothesis testing.
B) The exact sample size required to achieve a statistical power of 0.80.
C) A standardized, objective measure of the magnitude of an observed relationship or the
difference between groups.
D) The likelihood that the results can be generalized to the entire global population.
Answer: C
Explanation:
Effect size quantifies the strength of a phenomenon or the magnitude of a difference between
groups, independent of sample size. Unlike p-values, which only indicate if an effect exists, effect
size tells researchers how large and potentially important the effect actually is.
Question 3:
A researcher reports a Cohen's d of 0.85 when comparing the productivity of employees working
from home versus those in the office. How should this effect size be interpreted according to
standard guidelines?
A) It indicates a trivial or negligible difference between the two groups.
B) It indicates a small effect size, meaning the difference is barely noticeable.
C) It indicates a moderate effect size, suggesting a standard business impact.
D) It indicates a large effect size, suggesting a substantial and highly noticeable difference
between the two work environments.
Answer: D
Explanation:
Jacob Cohen proposed standard guidelines for interpreting d, where 0.2 is considered a small
effect, 0.5 is a medium effect, and 0.8 or higher is considered a large effect. A Cohen's d of 0.85
means the means of the two groups are separated by 0.85 standard deviations, which is a
substantial difference.
Question 4:
When creating data visualizations for a business report, why is it generally recommended that the
y-axis of a standard bar chart starts at zero?
A) Starting the y-axis at zero prevents the visual exaggeration of minor differences between
categories, ensuring honest data representation.
Q&A Verified Answers 2025
Course:
BUS5112 Quantitative Research Methods — University of the People (UoPeople)
Level:
MBA
Year:
2025/2026
Format:
Graded Quiz Solutions — 25 Q&A with Verified Answers
BUS5112 Unit 8 Graded Quiz — Research Reporting and Quality
25 Questions with Verified Answers | Score: 96/100
Question 1:
When distinguishing between statistical significance and practical significance in business
research, which statement is the most accurate?
A) Statistical significance is a subjective measure of business impact, while practical significance
is determined strictly by the p-value.
B) Statistical significance indicates that an effect likely exists, while practical significance
evaluates whether the magnitude of that effect is meaningful for real-world business decisions.
C) Practical significance guarantees that a research finding will generate higher revenue,
rendering statistical significance irrelevant.
D) They are identical concepts; any statistically significant finding is automatically practically
significant for a business.
Answer: B
Explanation:
Statistical significance (usually denoted by a p-value) simply tells us whether we can reject the
null hypothesis, meaning an effect likely exists and is not due to chance. Practical significance,
often measured by effect size, tells us if that effect is large enough to care about in a practical,
real-world business context. A finding can be statistically significant but practically meaningless.
, Question 2:
In quantitative reporting, what does "effect size" measure?
A) The probability that the researcher committed a Type I error during hypothesis testing.
B) The exact sample size required to achieve a statistical power of 0.80.
C) A standardized, objective measure of the magnitude of an observed relationship or the
difference between groups.
D) The likelihood that the results can be generalized to the entire global population.
Answer: C
Explanation:
Effect size quantifies the strength of a phenomenon or the magnitude of a difference between
groups, independent of sample size. Unlike p-values, which only indicate if an effect exists, effect
size tells researchers how large and potentially important the effect actually is.
Question 3:
A researcher reports a Cohen's d of 0.85 when comparing the productivity of employees working
from home versus those in the office. How should this effect size be interpreted according to
standard guidelines?
A) It indicates a trivial or negligible difference between the two groups.
B) It indicates a small effect size, meaning the difference is barely noticeable.
C) It indicates a moderate effect size, suggesting a standard business impact.
D) It indicates a large effect size, suggesting a substantial and highly noticeable difference
between the two work environments.
Answer: D
Explanation:
Jacob Cohen proposed standard guidelines for interpreting d, where 0.2 is considered a small
effect, 0.5 is a medium effect, and 0.8 or higher is considered a large effect. A Cohen's d of 0.85
means the means of the two groups are separated by 0.85 standard deviations, which is a
substantial difference.
Question 4:
When creating data visualizations for a business report, why is it generally recommended that the
y-axis of a standard bar chart starts at zero?
A) Starting the y-axis at zero prevents the visual exaggeration of minor differences between
categories, ensuring honest data representation.
