IB Maths AI HL 4.15 – Central Limit Theorem Notes

IB Maths AI HL 4.15 Notes – Central Limit Theorem

This IB Maths AI HL 4.15 resource covers Linear Combinations of Normal Variables and the Central Limit Theorem and is fully aligned with the IB Applications and Interpretation HL syllabus

Students learn that linear combinations of independent normal random variables remain normally distributed, and how to calculate the resulting mean and variance. The Central Limit Theorem is developed to show that the sample mean is approximately normally distributed for large samples, even when the parent distribution is not normal.

Structured practice questions guide students through modelling real contexts, finding distributions of combined variables, calculating probabilities, and applying the CLT to sample means.

Ideal for IB Maths AI HL teachers teaching advanced probability and sampling distributions.