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The Binomial Distribution
The binomial distribution models the number of successes in a fixed number of independent trials, each with the same probability of success — like counting heads in 10 coin flips. It is one of the most useful models in the course.
What you'll be able to do
- Recognise when a binomial model applies
- State the distribution X ~ B(n, p)
- Use the binomial probability formula
- Calculate P(X = r)
When to use it
A binomial model fits when there are a number of trials, each with only outcomes (success/failure) and the probability of success. We write .
The probability formula
The probability of exactly successes uses a binomial coefficient (the “choose” function from the Pure binomial expansion).
Example 1X ~ B(5, 0.5). Find P(X = 2).
Tip — The exponents must add to n: pʳ has r, (1−p) has n−r.
Conditions check
Always confirm the four conditions before modelling with a binomial: fixed n, independent trials, two outcomes, constant p. If any fails (e.g. without replacement changes p), the binomial does not apply.
Formula recap
Common mistakes to avoid
Key takeaways
- Binomial X ~ B(n, p): fixed n independent trials, constant p, two outcomes.
- P(X = r) = nCr · pʳ · (1−p)ⁿ⁻ʳ.
- Check the four conditions before using it.
Test yourself
Answer a few questions, then review anything you missed.
Check your understanding
Work through questions on The Binomial Distribution. Use the feedback to decide what needs another look.
6 past-paper questions · 12 marks
Related lessons
All 3 lessons- Same chapterProbability DistributionsUnderstanding discrete probability distributions.
- Same chapterCumulative ProbabilitiesCalculating cumulative probabilities for discrete distributions.
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