AP Statistics 7-Day Cram Plan

A full week is enough time to rebuild fluency in every major AP Statistics topic and walk into the exam confident on every inference type. This plan is roughly 3-4 hours per day with a final timed mock on Day 7.

If you only have 3 days, jump to our 3-day cram plan instead.

Day 1: Exploring One- and Two-Variable Data (3 hrs)

Why it matters: ~15% of exam points come from data exploration alone, and every inference question begins with describing the data.

Day 2: Sampling Methods + Experiments (3 hrs)

• SRS, stratified, cluster, systematic — know how each works and the kinds of bias each can prevent or introduce.
• Bias types: undercoverage, nonresponse, response bias, voluntary response.
• Experiments: random assignment, control group, blinding, blocking, matched pairs.
• Generalizability vs causation: random selection lets you generalize; random assignment lets you infer cause.

Practice: 15 MCQs + 1 study design FRQ.

💡 Score booster: Memorize the difference between random selection (lets you generalize to the population) and random assignment (lets you conclude cause-and-effect). FRQs love this distinction.

Day 3: Probability + Random Variables (3 hrs)

• Probability rules: addition, multiplication, conditional, complement.
• Independence: $P(A \cap B) = P(A) \cdot P(B)$ if and only if independent.
• Discrete random variables: $E(X)$, $\text{Var}(X)$, combining via $E(X \pm Y) = E(X) \pm E(Y)$ and (if independent) $\text{Var}(X \pm Y) = \text{Var}(X) + \text{Var}(Y)$.
• Binomial: $X \sim B(n, p)$, mean $np$, sd $\sqrt{np(1-p)}$.
• Geometric: $P(X = k) = (1-p)^{k-1} p$, mean $1/p$.
• Normal: $z$-scores, normalcdf, invNorm.

Practice: 1 random variables / probability FRQ + 15 MCQs.

Day 4: Sampling Distributions (3 hrs)

The conceptual heart of inference. Many students lose points here because they confuse $\sigma$ with $\sigma_{\hat p}$ or $\sigma_{\bar x}$.

• $\hat p$: mean $= p$, $\sigma_{\hat p} = \sqrt{\frac{p(1-p)}{n}}$.
• $\bar x$: mean $= \mu$, $\sigma_{\bar x} = \frac{\sigma}{\sqrt{n}}$.
• Central Limit Theorem: $\bar x$ is approximately normal when $n \geq 30$ (or population is already normal).
• Use the sampling distribution to find probabilities about $\hat p$ or $\bar x$ — this is a common MCQ trap.

Practice: 15 MCQs + 1 problem walking through "what's the probability that $\bar x$ exceeds $X$?"

Day 5: Confidence Intervals (3 hrs)

• General form: statistic $\pm$ (critical value)(standard error).
• 1-prop $z$: $\hat p \pm z^* \sqrt{\hat p (1 - \hat p)/n}$.
• 1-sample $t$: $\bar x \pm t^* \frac{s}{\sqrt{n}}$, $df = n - 1$.
• 2-prop $z$, 2-sample $t$, paired $t$.
• LinReg $t$: $b \pm t^* \cdot SE_b$.
• Interpretation in context: "We are 95% confident that the true mean weight of cereal boxes is between 15.7 and 16.3 oz."
• Confidence level interpretation: about repeated samples, NOT this specific interval.
• Conditions for $t$-procedures: random, 10%, normal (or $n \geq 30$, OR check graph for skewness/outliers if $n < 30$).

Practice: 1 CI FRQ (use full state-plan-do-conclude framework) + 15 MCQs.

Day 6: Significance Tests + Chi-Square (4 hrs)

• 4-step framework: State (hypotheses + parameter in context), Plan (name procedure + check conditions in context), Do (test statistic + p-value), Conclude (compare to $\alpha$, reject/fail to reject in context).
• Tests to drill: 1-prop $z$-test, 2-prop $z$-test, 1-sample $t$-test, 2-sample $t$-test, paired $t$-test.
• Chi-square: $\chi^2 = \sum \frac{(O-E)^2}{E}$, with three flavors:
  • Goodness-of-fit: tests one categorical variable against a claimed distribution.
  • Test for independence: tests two categorical variables from one sample.
  • Test for homogeneity: tests one categorical variable across multiple populations.
• Type I and II errors + power: be ready to identify them in context.

Practice: 1 $t$-test FRQ + 1 chi-square FRQ + 15 MCQs.

Day 7: Linear Regression Inference + Full Mock (3-4 hrs)

• Inference for slope: $\hat \beta_1 \pm t^* \cdot SE_{b}$ (CI), and $t = \frac{b}{SE_b}$ (test).
• Conditions: linear, independent, normal residuals, equal variance (Linear, Independent, Normal, Equal SD — sometimes called LINER without "R").
• Read computer output (Minitab, JMP-style): identify $b$, $SE_b$, $t$, $p$, $s$, $r^2$.

Then take an official mock exam under timed conditions:

• Section I: 40 MCQ in 90 min.
• Section II: 6 FRQs in 90 min (5 short + 1 investigative task).

Score yourself honestly using the official rubrics. Use the remaining time to review your weakest topics — not to learn new material.

Calculator drills (do throughout the week)

Practice these until they're automatic on your specific calculator:

1. normalcdf and invNorm for normal distribution probabilities.
2. binomcdf, binompdf, geomcdf, geompdf.
3. 1-PropZInt, 1-PropZTest, 2-PropZInt, 2-PropZTest.
4. TInterval, T-Test, 2-SampTInt, 2-SampTTest.
5. χ²-Test (matrix entry) and χ²GOF-Test (list entry).
6. LinRegTTest for slope inference.

What to do the night before

Open the last-minute review → for a one-page formula and trap checklist. Sleep 8 hours.

Recap: one week to peak

• 1 day on data exploration.
• 1 day on study design.
• 1 day on probability and random variables.
• 1 day on sampling distributions.
• 1 day on confidence intervals.
• 1 day on significance tests + chi-square.
• 1 day on regression inference + a full mock.

Start now: browse AP Statistics topics →.