PHIL 347 Week 6 Checkpoint Practice
Quiz Critical Reasoning —
Chamberlain University | 2026 Edition
DOMAIN 1: INDUCTIVE REASONING & STATISTICAL FALLACIES (8 Questions)
Question 1 — Multiple Choice (Hasty Generalization)
A first-semester nursing student arrives 10 minutes late to her first PHIL 347 lecture. The
professor firmly reminds the class of the attendance policy. That evening, the student texts her
study group: "All professors at this university are mean and inflexible. I'm dreading every class."
Which specific fallacy has this student committed?
A. Post Hoc Ergo Propter Hoc — assuming the professor's strictness caused her tardiness.
B. Hasty Generalization — drawing a broad conclusion about all professors from a single,
unrepresentative encounter. [CORRECT]
C. Gambler's Fallacy — believing her next professor will be kind to "balance out" the experience.
D. False Cause — mistaking correlation between tardiness and strictness for causation.
Rationale: The student has committed a Hasty Generalization by extrapolating a universal claim
about "all professors" from one anecdotal data point (a single professor on the first day). The
sample is both too small (n=1) and unrepresentative (first-day enforcement is standard
academic policy, not personal meanness). Strong inductive generalizations require a sufficiently
large and representative sample before broad population claims can be made.
Question 2 — True/False (Hasty Generalization)
A Hasty Generalization occurs whenever a speaker draws a broad conclusion about an entire
population from a sample that is too small or unrepresentative of that population.
A. True [CORRECT]
B. False
Rationale: True. A Hasty Generalization is formally defined as an inductive fallacy in which a
conclusion about all or most members of a population is drawn from a sample that is
, inadequate in size or unrepresentative in composition. The logical error is the inductive leap —
the gap between the limited evidence and the sweeping conclusion exceeds what the data can
support.
Question 3 — Select-All-That-Apply (Statistical Syllogism Criteria)
Which of the following are essential criteria for a strong statistical syllogism? (Select all that
apply.)
A. The sample size must be specifically large enough relative to the target population.
[CORRECT]
B. The sample must be randomly selected without any demographic stratification.
C. The sample must be representative of the target population in relevant characteristics.
[CORRECT]
D. The conclusion must be stated with absolute certainty rather than probability.
Rationale: A strong statistical syllogism requires two foundational conditions: (1) adequate
sample size — the sample must be large enough to minimize random error and yield a reliable
frequency estimate; and (2) representativeness — the sample must mirror the target
population on the variables relevant to the conclusion. Random selection is a common method
to achieve representativeness, but stratification is often necessary (not prohibited). Inductive
conclusions are probabilistic, never absolute.
Question 4 — Multiple Choice (Statistical Syllogism)
A pharmaceutical company tests a new blood-pressure medication on 12 male participants aged
22–25, all from the same university clinic. The drug shows a 90% reduction in symptoms. The
company advertises: "This drug is 90% effective for all adults with hypertension."
What is the primary logical flaw in this argument?
A. The conclusion commits the Gambler's Fallacy by assuming future patients will respond
similarly.
B. The argument is deductively valid but contains a false premise about blood pressure.
C. The sample is neither large enough nor representative of the target population (all adults
with hypertension). [CORRECT]
D. The argument violates the Principle of Charity by misrepresenting the drug's mechanism.
Rationale: The argument is a weak statistical syllogism because it violates both criteria for
inductive strength: (1) sample size — 12 participants is far too small to generalize to the entire
Quiz Critical Reasoning —
Chamberlain University | 2026 Edition
DOMAIN 1: INDUCTIVE REASONING & STATISTICAL FALLACIES (8 Questions)
Question 1 — Multiple Choice (Hasty Generalization)
A first-semester nursing student arrives 10 minutes late to her first PHIL 347 lecture. The
professor firmly reminds the class of the attendance policy. That evening, the student texts her
study group: "All professors at this university are mean and inflexible. I'm dreading every class."
Which specific fallacy has this student committed?
A. Post Hoc Ergo Propter Hoc — assuming the professor's strictness caused her tardiness.
B. Hasty Generalization — drawing a broad conclusion about all professors from a single,
unrepresentative encounter. [CORRECT]
C. Gambler's Fallacy — believing her next professor will be kind to "balance out" the experience.
D. False Cause — mistaking correlation between tardiness and strictness for causation.
Rationale: The student has committed a Hasty Generalization by extrapolating a universal claim
about "all professors" from one anecdotal data point (a single professor on the first day). The
sample is both too small (n=1) and unrepresentative (first-day enforcement is standard
academic policy, not personal meanness). Strong inductive generalizations require a sufficiently
large and representative sample before broad population claims can be made.
Question 2 — True/False (Hasty Generalization)
A Hasty Generalization occurs whenever a speaker draws a broad conclusion about an entire
population from a sample that is too small or unrepresentative of that population.
A. True [CORRECT]
B. False
Rationale: True. A Hasty Generalization is formally defined as an inductive fallacy in which a
conclusion about all or most members of a population is drawn from a sample that is
, inadequate in size or unrepresentative in composition. The logical error is the inductive leap —
the gap between the limited evidence and the sweeping conclusion exceeds what the data can
support.
Question 3 — Select-All-That-Apply (Statistical Syllogism Criteria)
Which of the following are essential criteria for a strong statistical syllogism? (Select all that
apply.)
A. The sample size must be specifically large enough relative to the target population.
[CORRECT]
B. The sample must be randomly selected without any demographic stratification.
C. The sample must be representative of the target population in relevant characteristics.
[CORRECT]
D. The conclusion must be stated with absolute certainty rather than probability.
Rationale: A strong statistical syllogism requires two foundational conditions: (1) adequate
sample size — the sample must be large enough to minimize random error and yield a reliable
frequency estimate; and (2) representativeness — the sample must mirror the target
population on the variables relevant to the conclusion. Random selection is a common method
to achieve representativeness, but stratification is often necessary (not prohibited). Inductive
conclusions are probabilistic, never absolute.
Question 4 — Multiple Choice (Statistical Syllogism)
A pharmaceutical company tests a new blood-pressure medication on 12 male participants aged
22–25, all from the same university clinic. The drug shows a 90% reduction in symptoms. The
company advertises: "This drug is 90% effective for all adults with hypertension."
What is the primary logical flaw in this argument?
A. The conclusion commits the Gambler's Fallacy by assuming future patients will respond
similarly.
B. The argument is deductively valid but contains a false premise about blood pressure.
C. The sample is neither large enough nor representative of the target population (all adults
with hypertension). [CORRECT]
D. The argument violates the Principle of Charity by misrepresenting the drug's mechanism.
Rationale: The argument is a weak statistical syllogism because it violates both criteria for
inductive strength: (1) sample size — 12 participants is far too small to generalize to the entire
