This Exam P sample reference tests Poisson Distribution. The zero-count probability is 0.90, so the Poisson mean is -ln(0.90). Subtracting the zero and one masses from one gives 0.0051755, which rounds to choice A.
These notes identify the calculation error associated with each wrong letter when that error is reproducible.
BThis squares the given positive-count probability, (0.10)² = 0.0100, as though two occurrences were two independent copies of the event. Poisson tail probabilities are built from count masses.
CThis simply halves the supplied 0.10 probability. There is no equal split between exactly one and at least two occurrences.
DThis reports the inferred Poisson mean, −ln(0.90) = 0.10536, rather than the upper-tail probability.
EThis exceeds 0.10, even though the event of at least two occurrences is contained in the event of at least one. It therefore violates the required subset bound before any detailed calculation.
Original practice · fully worked
Original variant: scale a fault rate across exposure lengths
Faults along a cable follow a homogeneous Poisson process. A two-kilometer test section has probability 0.64 of containing no faults. Calculate the probability that a five-kilometer section contains exactly one fault.
A 0.2856
B 0.3277
C 0.3656
D 0.6723
E 1.1157
Variant answer in brief
The one-kilometer zero probability is √(0.64)=0.8, so the five-kilometer mean is -5ln(0.8)=1.1157. Multiplying this mean by the five-kilometer zero probability 0.8⁵ gives 0.3656, choice C.
Setup
Setup
Let r be the fault rate per kilometer. The zero probability over length L is the Poisson zero mass with mean rL.
Pr(NL=0)=e−rL
Model
Model
Use the two-kilometer calibration to recover the rate and scale it to five kilometers.
e−2r=0.64
e−r=0.80
μ5=5r=−5ln(0.80)=1.1157177566
Compute
Compute
Evaluate the one-count Poisson mass using the five-kilometer mean and its zero probability.
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