HBX Core Final Exam | Verified Exam Questions and Answers | Latest
Updated Study Material 2026
Question:
A/B Test
Answer:
1) An experiment that compares the value of a
dependent variable (ex = likelihood that a website visitor purchases an item) across two different
groups (control group and treatment group). Members of each group must be RANDOMLY
SELECTED to ensure that the only difference between the groups is the "MANIPULATED"
independent variable (ex = size of the font on two otherwise identical websites)...a hypothesis
test that tests whether the means of the dependent variable are the same across the two groups;
also used to test whether another parameter - ex = standard deviation - is the same across two
groups
Question:
Adjusted R-Squared
Answer:
Measure of explanatory power of a
regression analysis. Adjusted R squared = R-squared*adjustment factor that decreases slightly as
each independent variable is added to regression model. Adjusted R-squared DROPS when a
new independent variable is added that does not improve the model's true explanatory power (vs.
R-squared which never decreases when a new independent variable is added to a regression
model). Adjusted R-Squared should always be used when comparing the explanatory power of
regression models that have different numbers of independent variables.
Question:
Alternative Hypothesis
Answer:
Theory or claim we are trying to
substantiate and stated as OPPOSITE OF A NULL HYPOTHESIS. When data allow us to
nullify the null hypothesis, we substantiate the alternative hypothesis.
Question:
Asymmetric Distribution
Answer:
Probability distribution that is not
, symmetric AROUND THE MEAN
Question:
Average/Mean
Answer:
For a distribution with discrete values, mean =
values of all data points in the set / number of data points
Question:
Base Case
Answer:
Category of categorical variable for which a dummy
variable is NOT included in a regression model. A regression model with a categorical variable
that has n categories should have n-1 dummy variables. The coefficients of the dummy variables
included in the regression model are interpreted in relation to the base case. The analyst can
select any category to be excluded from the regression model; however, different base cases lead
Question:
to different interpretations of the dummy variables' coefficients. Ex: Suppose we are trying to
determine the average difference in height between men and women in a sample, and suppose
that on average men are 5 inches taller than women in the sample. If we use Female as the base
case then the coefficient for the dummy variable for Male would be +5. If we use Male as the
base case, the coefficient for the dummy variable for Female would be -5.
Bias
Answer:
Tendency of a measurement process to over or under-
estimate the value of a population parameter. Although a sample statistic will almost always
differ from the population parameter, for an unbiased sample, the difference will be random. For
a biased sample, the statistic will differ in a systematic way. Common reasons for bias include
non-random sampling methods and non-neutral question phrasing.
Question:
Biased Sample
Answer:
Not representative of the population from which
it is collected. Sampling practices that can introduce bias include poorly phrased survey
questions and non-random sampling.
Updated Study Material 2026
Question:
A/B Test
Answer:
1) An experiment that compares the value of a
dependent variable (ex = likelihood that a website visitor purchases an item) across two different
groups (control group and treatment group). Members of each group must be RANDOMLY
SELECTED to ensure that the only difference between the groups is the "MANIPULATED"
independent variable (ex = size of the font on two otherwise identical websites)...a hypothesis
test that tests whether the means of the dependent variable are the same across the two groups;
also used to test whether another parameter - ex = standard deviation - is the same across two
groups
Question:
Adjusted R-Squared
Answer:
Measure of explanatory power of a
regression analysis. Adjusted R squared = R-squared*adjustment factor that decreases slightly as
each independent variable is added to regression model. Adjusted R-squared DROPS when a
new independent variable is added that does not improve the model's true explanatory power (vs.
R-squared which never decreases when a new independent variable is added to a regression
model). Adjusted R-Squared should always be used when comparing the explanatory power of
regression models that have different numbers of independent variables.
Question:
Alternative Hypothesis
Answer:
Theory or claim we are trying to
substantiate and stated as OPPOSITE OF A NULL HYPOTHESIS. When data allow us to
nullify the null hypothesis, we substantiate the alternative hypothesis.
Question:
Asymmetric Distribution
Answer:
Probability distribution that is not
, symmetric AROUND THE MEAN
Question:
Average/Mean
Answer:
For a distribution with discrete values, mean =
values of all data points in the set / number of data points
Question:
Base Case
Answer:
Category of categorical variable for which a dummy
variable is NOT included in a regression model. A regression model with a categorical variable
that has n categories should have n-1 dummy variables. The coefficients of the dummy variables
included in the regression model are interpreted in relation to the base case. The analyst can
select any category to be excluded from the regression model; however, different base cases lead
Question:
to different interpretations of the dummy variables' coefficients. Ex: Suppose we are trying to
determine the average difference in height between men and women in a sample, and suppose
that on average men are 5 inches taller than women in the sample. If we use Female as the base
case then the coefficient for the dummy variable for Male would be +5. If we use Male as the
base case, the coefficient for the dummy variable for Female would be -5.
Bias
Answer:
Tendency of a measurement process to over or under-
estimate the value of a population parameter. Although a sample statistic will almost always
differ from the population parameter, for an unbiased sample, the difference will be random. For
a biased sample, the statistic will differ in a systematic way. Common reasons for bias include
non-random sampling methods and non-neutral question phrasing.
Question:
Biased Sample
Answer:
Not representative of the population from which
it is collected. Sampling practices that can introduce bias include poorly phrased survey
questions and non-random sampling.
