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v0.1.894
NotesMath AI HLTopic 4.9
Unit 4 · Statistics & Probability · Topic 4.9

IB Math AI HL — Normal distribution

IB Mathematics AI SL topic covering core concepts and exam-style applications.

Exam technique guidePractice questions

Key concepts in Normal distribution

Key Idea: The normal distribution is a symmetric, bell-shaped curve that models many naturally occurring quantities — heights, test scores, measurement errors. It is defined by two parameters: the mean μ (centre of the bell) and standard deviation σ (width of the bell). Almost all normal distribution work in IB Math AI SL is done on the GDC.

✅ Key properties of the normal distribution


📊 GDC functions for the normal distribution

Example: X ~ N(60, 25), so μ = 60, σ = 5 P(X < 55) = normalcdf(−1E99, 55, 60, 5) = 0.159 P(55 < X < 70) = normalcdf(55, 70, 60, 5) = 0.819 P(X > 68) = normalcdf(68, 1E99, 60, 5) = 0.0548 Find x where P(X < x) = 0.90: x = invNorm(0.90, 60, 5) = 66.4
The second parameter in X ~ N(μ, σ²) is variance, not standard deviation. If σ² = 25, then σ = 5. Always enter σ (not σ²) into GDC functions. For 'at least x' problems: P(X > x) = 1 − P(X ≤ x), or use normalcdf with 1E99 as the upper bound.
Paper 2 (GDC allowed): Write X ~ N(μ, σ²) and the probability statement before calculating. Show the GDC function used and its output. Inverse normal context: 'Find the value of x such that 25% of scores exceed x.' This means P(X > x) = 0.25, so P(X < x) = 0.75. Use invNorm(0.75, μ, σ).

IB-style question [7 marks]

The lifetime of a brand of light bulb is normally distributed with a mean of 1200 hours and a standard deviation of 100 hours. Let X be the lifetime of a randomly chosen bulb. (a) Find the probability that a bulb lasts less than 1000 hours. (b) Find the probability that a bulb lasts between 1100 and 1300 hours. (c) The manufacturer offers a free replacement for the 5% of bulbs with the shortest lifetimes. Find the lifetime below which a bulb qualifies for a free replacement.

Step by step:

  1. Write down the distribution. The mean is μ = 1200 and the standard deviation is σ = 100.

    X∼N(1200, 1002)X \sim N(1200,\ 100^2)X∼N(1200, 1002)
  2. (a) 'Less than 1000' is a left-tail area; use a very small lower bound on the GDC.

    P(X<1000)=normalcdf(−1E99, 1000, 1200, 100)P(X < 1000) = \text{normalcdf}(-1\mathrm{E}99,\ 1000,\ 1200,\ 100)P(X<1000)=normalcdf(−1E99, 1000, 1200, 100)
  3. Read off the GDC.

    P(X<1000)=0.0228P(X < 1000) = 0.0228P(X<1000)=0.0228
  4. (b) Use both bounds directly for the 'between' area.

    P(1100<X<1300)=normalcdf(1100, 1300, 1200, 100)P(1100 < X < 1300) = \text{normalcdf}(1100,\ 1300,\ 1200,\ 100)P(1100<X<1300)=normalcdf(1100, 1300, 1200, 100)
  5. Read off the GDC.

    P(1100<X<1300)=0.683P(1100 < X < 1300) = 0.683P(1100<X<1300)=0.683
  6. (c) The shortest 5% lie below the cut-off, so the cumulative (left) area is 0.05. Use the inverse normal.

    k=invNorm(0.05, 1200, 100)k = \text{invNorm}(0.05,\ 1200,\ 100)k=invNorm(0.05, 1200, 100)
  7. Read off the cut-off lifetime.

    k=1040 hoursk = 1040\ \text{hours}k=1040 hours
Final answer:

(a) 0.0228. (b) 0.683. (c) 1040 hours (3 s.f.) — bulbs lasting under about 1040 hours qualify.

What you'll learn in Topic 4.9

  • 4.9.1 Normal Distribution Properties
  • 4.9.2 Normal Probabilities
Suggested study order: Read the notes for each sub-topic below → test yourself with flashcards → attempt practice questions → review exam technique.

Study resources — 4.9 Normal distribution

4.9.1

Normal Distribution Properties

Notes
4.9.2

Normal Probabilities

Notes

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Topic 4.9 Normal distribution forms a core part of Unit 4: Statistics & Probability in IB Math AI HL. Mastering these concepts will strengthen your understanding of connected topics across the syllabus and prepare you for exam questions that require analysis, evaluation, and real-world application.

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