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AP Statistics
1 Exploring One-Variable Data and Collecting Data
2 Probability, Random Variables, and Probability Distributions
2.1 Tabular and Graphical Representations for the Distributions of Two Categorical Variables
2.2 Summary Statistics for Two Categorical Variables
2.3 Estimating Probabilities Using Simulation
2.4 Introduction to Probability
2.5 Mutually Exclusive Events
2.6 Conditional Probability
2.7 Independent Events and Unions of Events
2.8 Introduction to Random Variables and Probability Distributions
2.9 Parameters of Random Variables
2.10 The Binomial Distribution
2.11 The Normal Distribution
2.11.A—Describe a normal distribution
2.11.B—Calculate the mean and standard deviation for a normal distribution
2.11.C—Calculate percentages from a normal distribution using the empirical rule
2.11.D—Calculate the probability that a particular value lies within a given interval of a normal distribution
2.11.E—Calculate the associated intervals and areas of a normal distribution
2.11.F—Compare measures of relative position for distributions
2.12 Sampling Distributions and the Central Limit Theorem
3 Inference for Categorical Data: Proportions
4 Inference for Quantitative Data: Means
5 Regression Analysis
AP/Statistics/Concept//2 Probability, Random Variables, and Probability Distributions/2.11 The Normal Distribution/2.11.B—Calculate the mean and standard deviation for a normal distribution

2 Probability, Random Variables, and Probability Distributions

Syllabus
2026
Section
2
Level
—

Exam analysis

Most tested topics

  1. Tabular and Graphical Representations for the Distributions of Two Categorical Variables
  2. Summary Statistics for Two Categorical Variables
  3. Estimating Probabilities Using Simulation
  4. Introduction to Probability
  5. Mutually Exclusive Events
  6. Conditional Probability
  7. Independent Events and Unions of Events
  8. Introduction to Random Variables and Probability Distributions
  9. Parameters of Random Variables
  10. The Binomial Distribution
  11. The Normal Distribution
  12. Sampling Distributions and the Central Limit Theorem