The conjunction fallacy occurs when someone:

The conjunction fallacy occurs when someone:

• A. Judges a conjunction (A and B) as more probable than one of its components
• B. Uses base rates to interpret a test
• C. Ignores sample size
• D. Confuses correlation with causation

Answer: (A)

Conjunction fallacy happens when people treat a conjunction of two events as more probable than one of its components. In probability, the chance that both A and B occur cannot exceed the chance that either A or B occurs alone; the conjunction is a more specific situation, so its probability is at most equal to the probability of its parts. This is why choosing the description that judges a conjunction (A and B) as more probable than one of its components is the correct characterization. It captures the classic error: thinking the combined scenario is likelier because it seems to fit the description so well, ignoring that adding a condition makes it harder, not easier, to occur. The other options describe different reasoning mistakes, not the conjunction fallacy: using base rates to interpret a test points to base-rate considerations; ignoring sample size refers to sample-size bias; and confusing correlation with causation is a separate logical pitfall.

Conjunction fallacy happens when people treat a conjunction of two events as more probable than one of its components. In probability, the chance that both A and B occur cannot exceed the chance that either A or B occurs alone; the conjunction is a more specific situation, so its probability is at most equal to the probability of its parts.

This is why choosing the description that judges a conjunction (A and B) as more probable than one of its components is the correct characterization. It captures the classic error: thinking the combined scenario is likelier because it seems to fit the description so well, ignoring that adding a condition makes it harder, not easier, to occur.

The other options describe different reasoning mistakes, not the conjunction fallacy: using base rates to interpret a test points to base-rate considerations; ignoring sample size refers to sample-size bias; and confusing correlation with causation is a separate logical pitfall.