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).