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October 18, 2026

Discrete random variables, binomial and Poisson distributions

ZIMSEC A Level · Statistics · Form 5, Form 6

Discrete random variables

A probability distribution lists values x with probabilities summing to 1.

E(X) = Σx P(X = x); Var(X) = Σx² P(X = x) − [E(X)]². E(aX + b) = aE(X) + b; Var(aX + b) = a²Var(X).

The binomial distribution, X ~ B(n, p)

Conditions: fixed number of independent trials, two outcomes, constant probability p.

P(X = r) = ⁿCᵣ pʳ(1 − p)ⁿ⁻ʳ; mean np; variance np(1 − p).

Worked example: 30% of seeds fail to germinate. In a packet of 10, find P(exactly 2 fail).

P(X = 2) = ¹⁰C₂ × 0.3² × 0.7⁸ = 45 × 0.09 × 0.05765 = 0.2335

The Poisson distribution, X ~ Po(λ)

Models random events in a fixed interval of time or space, occurring independently at a constant average rate λ.

P(X = r) = e^(−λ) λʳ/r!; mean = variance = λ.

Worked example: A clinic receives an average of 3 emergency calls per hour. Find P(no calls in an hour) and P(at most 1 call).

P(X = 0) = e⁻³ = 0.0498

P(X ≤ 1) = e⁻³(1 + 3) = 0.199

The Poisson can approximate B(n, p) when n is large and p is small (λ = np).

Exam tip: Check the conditions of a distribution in context – examiners often ask why a model is suitable.

Check yourself

  1. X ~ B(8, 0.5). Find E(X) and Var(X).
  2. X ~ Po(2). Find P(X = 2).
  3. State two conditions for a binomial distribution.
Show answers
  1. E(X) = 4; Var(X) = 2
  2. e⁻² × 4/2 = 0.271
  3. Any two: fixed number of trials, independent trials, two outcomes, constant probability of success.

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