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AP® Statistics · Unit 2 · Topic 2.11

The Normal Distribution: Revision Notes (Statistics 2.11)

One-page recap of continuous random variables, normal curves, the standard normal distribution, the 68–95–99.7 rule, areas, boundary values and percentiles, with the mistakes that cost marks.

Short on time? This page is the recap. For explanations and worked examples, use the full study guide.

Recap

  • A continuous random variable can take any value in an interval. Intervals have probabilities; a single exact value has probability 0.
  • A normal curve is continuous, unimodal, symmetric and bell-shaped. It can model a data distribution or a random variable.
  • It is fixed by μ (centre) and σ (spread). Smaller σ: taller and more concentrated. Larger σ: shorter and more spread out.
  • The standard normal distribution is N(0, 1).
  • Percentiles from a normal model let you compare positions within or between distributions.

Key relationships

Idea Rule
Standardise z = (x − μ) / σ; back again: x = μ + zσ
Empirical rule about 68%, 95%, 99.7% within 1, 2, 3 σ of μ
Strips (one side) 34%, 13.5%, 2.35%, 0.15%
Interval probability P(a < X < b) = normalcdf(a, b, μ, σ)
Lowest p% P(X < xₐ) = p/100
Highest p% P(X > x_b) = p/100, so area to the left is 1 − p/100
Middle p% (1 − p/100)/2 in each tail
Most extreme p% ½(p/100) in each tail

Assumptions and conventions

  • Use a normal model only when the variable is roughly unimodal and symmetric. Check the context: can the values go below 0?
  • A z-table gives the area to the left of z, with z to 2 decimal places. Table and technology answers may differ slightly.
  • Show four things: distribution and parameters, boundary, direction (sketch), answer.

Mistakes to avoid

  1. Dividing by σ² instead of σ in a z-score.
  2. Wrong tail: forgetting 1 − area for “more than” or “highest”.
  3. Putting the whole “most extreme p%” in one tail.
  4. Quoting the empirical rule as exact. Say “about”.
  5. A bare calculator command with no labels or context.
  6. Comparing raw values from different distributions instead of percentiles.

Quick self-check

X ~ N(40, 5).

  1. Use the empirical rule to estimate the percentage of values above 50. (50 is μ + 2σ, so about 2.5%)
  2. Find P(X < 33). (z = −1.4; 0.0808)
  3. Find the value that separates the top 20% from the rest. (area to the left 0.80, z = 0.8416, x = 44.21)

Next: practice questions.

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