This Exam P sample reference tests Conditional Expectation. The joint expectation of the advanced count times the early-stage condition indicator is 6(0.1)(1-0.8⁵)=0.403392. Dividing by P(at least one early)=1-0.8⁶ gives 0.546708, choice C.
These notes identify the calculation error associated with each wrong letter when that error is reproducible.
AThe value 0.403 is the unnormalized joint expectation E[Y 1_A]. It must be divided by P(A)=0.737856.
BThe value 0.500 treats the condition as exactly one fixed early-stage patient, leaving five patients each with unconditional advanced probability 0.10.
DThe value 0.600 is the unconditional expectation 6(0.10), ignoring the information about the early-stage count.
EThe value 0.625 is 5(0.10/0.80), combining an exactly-one-early assumption with an inappropriate re-normalization of each remaining patient.
Original practice · fully worked
Original variant: conditional count from Poisson splitting
Independent event counts A and B are Poisson with means 2 and 3, respectively. Given that their combined count equals 4, calculate the conditional expected value of A.
A 1.2
B 1.6
C 2.0
D 2.4
E 4.0
Variant answer in brief
Conditioned on a combined count of four, the type-A count is Binomial(4,2/5). Its expectation is 4(2/5)=1.6, choice B.
Setup
Setup
Combine the two independent Poisson streams.
A+B∼Poisson(2+3)
Model
Model
Given the total, each event is classified as type A with probability proportional to its rate.
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