This IB Math AI SL 4.8 – Binomial Distribution resource develops students’ understanding of how repeated two-outcome experiments are modelled using the binomial distribution. Learners identify the four binomial conditions, use the notation (X \sim \text{Bin}(n,p)), and apply the binomial formula to calculate exact probabilities, as well as mean, variance, and standard deviation . Real contexts such as free throws, durability testing, biased coins, and defect rates help students connect probability models to practical decision-making situations.
Structured tasks move from checking whether situations are binomial to computing probabilities, using complements for “at least” questions, and solving “between” probability problems. Extended questions develop deeper algebraic reasoning, including finding unknown §, determining the smallest (n) to achieve a target probability, comparing consistency via standard deviation, and working within one standard deviation of the mean. With scaffolded practice, exam-style skills, challenging extension tasks, and a full answer key, this resource supports SL classroom teaching and independent study in line with IB Mathematics AI expectations.
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