College Board · AP Statistics

20 Essential MCQ Problems

All units covered · Exam-style difficulty · Instant feedback with detailed explanations

20QUESTIONS
7UNITS
45MINUTES
5AP SCORE
REVIEW

Key Concepts & Formulas to Memorize

Unit 1 · Exploring Data

Describing Distributions

Always describe Shape, Center, Spread, and Outliers (SOCS). Use mean & SD for symmetric distributions; median & IQR for skewed data.

IQR = Q3 − Q1 Outlier: x < Q1 − 1.5·IQR or x > Q3 + 1.5·IQR
Unit 1 · Exploring Data

Normal Distribution & z-scores

A z-score tells how many standard deviations a value is from the mean. Use Table A or calculator to find area (probability).

z = (x − μ) / σ 68–95–99.7 Rule: μ ± 1σ → 68% μ ± 2σ → 95% μ ± 3σ → 99.7%
Unit 2 · Bivariate Data

Least-Squares Regression

The LSRL minimizes the sum of squared residuals. The slope b tells the predicted change in ŷ per unit increase in x. Always check the residual plot for linearity.

ŷ = a + bx b = r · (sy/sx) a = ȳ − b·x̄ r² = proportion of variation explained
Unit 3 · Sampling & Experiments

Sampling Methods & Bias

SRS: every sample of size n equally likely. Stratified: random sample within groups. Cluster: randomly select groups entirely. Voluntary response and convenience samples produce bias.

Undercoverage bias → sampling frame Nonresponse bias → selected won't respond Response bias → question wording
Unit 3 · Experiments

Experimental Design

Control, Randomize, Replicate. A well-designed experiment can establish causation. Observational studies can only show association. Placebo effect requires a control group; blinding prevents bias.

Completely Randomized Design Randomized Block Design Matched Pairs Design
Unit 4 · Probability

Probability Rules

Addition rule (general): P(A∪B) = P(A)+P(B)−P(A∩B). Multiplication rule: P(A∩B) = P(A)·P(B|A). Independent if P(A∩B) = P(A)·P(B).

P(A|B) = P(A∩B) / P(B) Complement: P(Aᶜ) = 1 − P(A)
Unit 4 · Random Variables

Discrete & Continuous RVs

Expected value E(X) = Σ x·P(x). For independent random variables: μ(X±Y) = μX ± μY and σ²(X±Y) = σ²X + σ²Y (variances always add).

Var(X) = Σ(x − μ)²·P(x) σ(X+Y) = √(σ²X + σ²Y) (only when X, Y independent)
Unit 4 · Distributions

Binomial & Geometric

Binomial BINS: Binary, Independent, Number fixed, Same p. Geometric: counts trials until first success.

Binomial: P(X=k) = C(n,k)·pᵏ·(1−p)ⁿ⁻ᵏ μ = np, σ = √(np(1−p)) Geometric: μ = 1/p
Unit 5 · Sampling Distributions

Central Limit Theorem

For large n, the sampling distribution of x̄ is approximately Normal regardless of population shape. Typically n ≥ 30 is sufficient; smaller n OK if population is roughly Normal.

μ(x̄) = μ σ(x̄) = σ / √n z = (x̄ − μ) / (σ/√n)
Unit 6–7 · Inference

Confidence Intervals & Tests

CI = statistic ± (critical value)(SE). A 95% CI means: if we repeated sampling many times, 95% of such intervals would capture the true parameter. P-value = probability of observing our result or more extreme, given H₀ is true.

Type I Error: reject true H₀ (α) Type II Error: fail to reject false H₀ (β) Power = 1 − β Wider CI → lower confidence level ✗
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