The Binomial Distribution

Model fixed-trial experiments with the binomial distribution at A-Level. Calculate probabilities and use cumulative tables.

The binomial distribution models the number of successes in nn independent trials, each with probability pp of success.

Conditions

  • Fixed number of trials nn.
  • Each trial: success (probability pp) or failure (probability q=1pq = 1-p).
  • Trials are independent.
  • pp is constant.

Formula

XB(n,p)X \sim B(n, p)

P(X=r)=(nr)pr(1p)nrP(X = r) = \binom{n}{r} p^r (1-p)^{n-r}

Mean and Variance

E(X)=npE(X) = np, Var(X)=np(1p)\text{Var}(X) = np(1-p).

Worked Example: Example 1

Problem

XB(10,0.3)X \sim B(10, 0.3). Find P(X=3)P(X = 3).

P(X=3)=(103)(0.3)3(0.7)7=120×0.027×0.08240.267P(X=3) = \binom{10}{3}(0.3)^3(0.7)^7 = 120 \times 0.027 \times 0.0824 \approx 0.267.

Solution

Worked Example: Example 2

Problem

P(X2)P(X \leq 2) = sum of P(X=0)+P(X=1)+P(X=2)P(X=0) + P(X=1) + P(X=2). Or use cumulative binomial tables.

Solution

Hypothesis Testing with Binomial

Test whether pp has changed from a claimed value using the binomial distribution as the test statistic.

Practice Problems

    1. XB(8,0.4)X \sim B(8, 0.4). Find P(X=5)P(X = 5).
    1. Find E(X)E(X) and Var(X)\text{Var}(X) for B(20,0.35)B(20, 0.35).
    1. P(X1)P(X \geq 1) for XB(5,0.1)X \sim B(5, 0.1).

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Key Takeaways

  • XB(n,p)X \sim B(n, p): nn trials, probability pp, independent.

  • P(X=r)=(nr)prqnrP(X = r) = \binom{n}{r}p^r q^{n-r}.

  • E(X)=npE(X) = np. Var(X)=npq\text{Var}(X) = npq.

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