AP Statistics / Unit 3: Inference for Categorical Data: Proportions / Topic 3.12
NUM8ERS study notes · Topic 3.12

Setting Up a Test for the Difference Between Two Population Proportions

Before pressing a calculator button, make a clear plan. Define the 22 population proportions, write the hypotheses and check whether a two-proportion zz-test is appropriate.

2026–27 curriculum6 worked examples10 practice questions6 visual guides

By the end of this lesson, you should be able to:

  • Recognize a question about 22 population proportions and choose the testing method.
  • Define both parameters using the populations and the counted response.
  • Write a null hypothesis of no difference and a suitable alternative.
  • Calculate the pooled sample proportion and 44 expected counts.
  • Justify randomization, independence, the 10%10\% checks when needed and normality.
  • Explain when the usual test setup is not justified.

Before you start: Review one-proportion test setup and Topic 3.11’s interval-based claims. A test investigates a stated population claim; a sample difference alone is not a conclusion.

First time learning this? Follow the school study from the question to a complete test plan.

Here to revise? Use the setup checklist, then try the questions before opening hints or solutions.

The concept in 60 seconds

School A wants to know whether its students are more likely to prefer digital study notes than students at School B. Independent random samples show 124124 of 200200 students at A and 6969 of 150150 students at B prefer digital notes.

The sample proportions are 0.620.62 and 0.460.46, a difference of 0.160.16, or 1616 percentage points. Could a difference like this arise from sampling variation when the population proportions are actually equal? That is the question a test can address.

The setup has four jobs: define the population parameters, choose a two-sample zz-test for a difference in proportions, state H0H_{0} and HaH_{a}, and justify the conditions. The test statistic, pp-value and final decision follow in Topic 3.13.

Visual guide 1: connect the question, parameters and plan
Question set in advanceIs A’s rate higher?

Same binary preference outcome, 22 school populations.

Unknown targetp1−p2p_{1}-p_{2}

p1p_{1} is A’s population preference proportion; p2p_{2} is B’s. Keep A minus B throughout.

Test setupH0:p1−p2=0H_0:p_1-p_2=0

Ha:p1−p2>0H_a:p_1-p_2\gt0. Justify independent random samples, each 10%10\% check and 44 pooled expected counts.

The observed 0.160.16 difference is sample evidence. Hypotheses concern the unknown population difference, and the plan must be justified before interpreting a test result.

Here the research question was “Is A higher?” before the results were examined. Therefore, with A minus B as our order:

H0:p1−p2=0H_0:p_1-p_2=0 — the population preference proportions are equal.

Ha:p1−p2>0H_a:p_1-p_2\gt0 — School A’s population preference proportion is higher.

Quick check: does the sample difference of 0.160.16 prove that A’s population proportion is higher?

No. It is observed sample evidence. A justified test evaluates how unusual that evidence would be under H0H_{0}; the setup alone does not establish a population difference.

Define the 22 population parameters

A strong parameter definition names the population, the response category counted as a success and the group order. “Success” is just the category we count; it need not be a desirable outcome.

p1p_{1}: School A

The proportion of all students at School A who prefer digital study notes.

It is an unknown population value. Its sample estimate is p^1=124200=0.62\hat{p}_{1}=\frac{124}{200}=0.62.

p2p_{2}: School B

The proportion of all students at School B who prefer digital study notes.

Its sample estimate is p^2=69150=0.46\hat{p}_{2}=\frac{69}{150}=0.46. Use the same preference question and success definition in both groups.

Our target is p1−p2p_{1}-p_{2}, the population difference A minus B. The sample statistic p^1−p^2\hat{p}_{1}-\hat{p}_{2} estimates this difference. Hypotheses concern the population parameters p1p_{1} and p2p_{2}, not the observed sample proportions.

The school study: keep population parameters, observed statistics and null-model quantities separate.
SymbolMeaningSchool value or role
p1p_{1}, p2p_{2}Population proportions with the same digital-notes preferenceUnknown; A and B, respectively
p1−p2p_{1}-p_{2}Population difference being testedA minus B
x1x_{1}, x2x_{2}Observed success counts124124 and 6969
n1n_{1}, n2n_{2}Sample sizes200200 and 150150
N1N_{1}, N2N_{2}Population sizes10,00010{,}000 and 8,0008{,}000
p^1\hat{p}_{1}, p^2\hat{p}_{2}Observed sample proportions0.620.62 and 0.460.46
p^1−p^2\hat{p}_{1}-\hat{p}_{2}Observed sample difference0.16=16 percentage points0.16=16\,\text{percentage points}
p^c\hat{p}_{c}Combined sample proportion used under H0H_{0}193350≈0.5514\frac{193}{350}\approx 0.5514
H0H_{0}Equality of the population ratesp1−p2=0p_{1}-p_{2}=0
HaH_{a}Departure chosen from the research questionp1−p2>0p_{1}-p_{2} > 0

For a randomized experiment

Define the parameters as the underlying success probabilities or success proportions under the 22 treatments in the population or experimental setting of interest. Name each treatment and the same response outcome. Do not automatically claim that volunteer participants represent every student or every customer.

Quick check: what is missing from “p1p_{1} is the proportion who said yes”?

The definition does not identify the population or explain what “yes” means. Write, for example, “p1p_{1} is the proportion of all School A students who prefer digital study notes.”

Write the null and alternative hypotheses

The usual AP two-proportion zz-test here investigates no population difference as the null. You can write this in either equivalent form:

H0:p1=p2H_0:p_1=p_2 or H0:p1−p2=0H_0:p_1-p_2=0.

The alternative describes the departure of interest. First define your order, then translate the original research question:

Choose the alternative for group 1 minus group 2. The null is p1−p2=0p_{1}-p_{2}=0 in every row.
Original questionAlternative hypothesisDirection
Is group 1’s success proportion higher?Ha:p1−p2>0H_{a}: p_{1}-p_{2} > 0One-sided: higher
Is group 1’s success proportion lower?Ha:p1−p2<0H_{a}: p_{1}-p_{2} < 0One-sided: lower
Are the population proportions different?Ha:p1−p2≠0H_{a}: p_{1}-p_{2} \ne 0Two-sided: either direction
Visual guide 2: the alternative chooses the evidence direction
Alternative directions for a two-proportion testThree identical approximate null sampling curves for the sample-proportion difference, centered at zero. The higher alternative has a right arrow; the lower alternative a left arrow; the different alternative arrows both ways. All panels use a common minus 20 to plus 20 percentage-point scale. A dotted line marks zero. Arrows indicate directions, not rejection cutoffs or p-value areas. −20 0 20 Higher: Hₐ: p₁ − p₂ > 0 −20 0 20 Lower: Hₐ: p₁ − p₂ < 0 −20 0 20 Sample difference (percentage points) Different: Hₐ: p₁ − p₂ ≠ 0 The research question sets the alternative direction Same approximate null model in every panel. H₀: p₁ − p₂ = 0. Dotted line: zero difference. Arrows: directions that challenge H₀. Arrows show directions, not rejection cutoffs or p-value areas. Curve height is density. School setup: pooled estimate ≈ 0.5514; n₁ = 200, n₂ = 150. The observed sample does not choose a new alternative. Alternative directions for a two-proportion testThree identical approximate null sampling curves for the sample-proportion difference, centered at zero. The higher alternative has a right arrow; the lower alternative a left arrow; the different alternative arrows both ways. All panels use a common minus 20 to plus 20 percentage-point scale. A dotted line marks zero. Arrows indicate directions, not rejection cutoffs or p-value areas. −20 0 20 Higher: Hₐ: p₁ − p₂ > 0 −20 0 20 Lower: Hₐ: p₁ − p₂ < 0 −20 0 20 Sample difference (percentage points) Different: Hₐ: p₁ − p₂ ≠ 0 The question sets the alternative direction Same approximate null model H₀: p₁ − p₂ = 0 Dotted line: zero difference Arrows are directions, not cutoffs. Curve height is density, not probability. Pooled estimate ≈ 0.5514
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