This Exam P sample reference tests Conditional Probability. Inclusion–exclusion gives joint working probability 0.8+0.6-0.9=0.5. Dividing by the probability that A works gives P(B works given A works)=0.5/0.8=5/8, choice C.
These notes identify the calculation error associated with each wrong letter when that error is reproducible.
AThe value 1/2 is the joint probability P(A and B). It is the numerator before conditioning on A.
BThe value 3/5 is the marginal probability P(B). It would equal P(B|A) only under independence, which is not stated and is inconsistent with the union.
DThe value 3/4 is 0.6/0.8, dividing the marginal of B by the marginal of A rather than using the joint probability.
EThe value 5/6 is 0.5/0.6=P(A|B), which reverses the conditioning direction.
Original practice · fully worked
Original variant: recover indicator covariance from an exactly-one probability
Events A and B satisfy P(A)=0.70, P(B)=0.50, and P(exactly one of A and B)=0.40. Let I_A and I_B be their event indicators. Calculate Cov(I_A,I_B).
A -0.05
B 0
C 0.05
D 0.35
E 0.40
Variant answer in brief
If x is the intersection probability, exactly one has probability 0.70+0.50-2x. Setting this to 0.40 gives x=0.40, so covariance is 0.40-(0.70)(0.50)=0.05, choice C.
Setup
Setup
Let x be the joint-event probability and express the exactly-one region through x.
Pr(exactly one)=Pr(A)+Pr(B)−2Pr(A∩B)
0.40=0.70+0.50−2x
Model
Model
Solve for the joint probability and use the covariance identity for indicators.
x=0.40
Cov(IA,IB)=E[IAIB]−E[IA]E[IB]
Compute
Compute
Substitute the joint probability and two marginals.
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