AP Statistics / Unit 4: Inference for Quantitative Data: Means / Topic 4.4
NUM8ERS study notes · Topic 4.4

Setting Up a Test for a Population Mean or Population Mean Difference

Turn a question about an average into a clear test plan. Define the population mean, write the hypotheses, choose a one-sample tt-test, and check the conditions before calculating.

2026–27 curriculumOne-sample tt-test setupMatched pairs4 worked examples8 practice questions
DefineThe population parameter and units
PlanH0H_0, HaH_a and the appropriate procedure
JustifyRandomization, independence and shape

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

  • Choose a one-sample tt-test for 11 mean or a mean paired difference.
  • Define the parameter in context and write null and alternative hypotheses.
  • Translate “lower,” “higher” or “different” into the correct alternative.
  • Justify the relevant randomization, 10%10\% and sample-data conditions.

Before you start: Review population means, sample SD⁡\operatorname{SD}, paired differences and the role of a null hypothesis.

First time learning this? Follow the bottle investigation, then work through the paired example.

Here to revise? Use the setup checklist, then attempt the practice questions before revealing solutions.

The concept in 60 seconds

Setting up a test means deciding what to test and why the method is appropriate. For quantitative measurements from 11 population with unknown population SD⁡\operatorname{SD}, use a one-sample tt-test for a population mean.

For matched pairs, calculate a difference within each pair. Those differences form 11 sample, so use a one-sample tt-test for the population mean difference.

11 mean: H0:μ=μ0H_0:\mu=\mu_0. Choose Ha:μ<μ0H_a:\mu\lt\mu_0, μ>μ0\mu > \mu_0 or μ≠μ0\mu \ne \mu_0 to match the investigative question.

Paired mean difference: Define dd in a stated order. For no average difference, write H0:μd=0H_0:\mu_d=0. Choose the alternative using that same order.

The hypotheses concern population parameters, not the observed sample mean. The sample is the evidence you will use to investigate the hypotheses; it is not the hypothesis itself.

This lesson builds the setup. Topic 4.5 develops the test statistic, pp-value and final conclusion.

A bottle-filling investigation

Fictional teaching investigation: A team asks whether the mean fill volume of bottles in a particular lot differs from 500 mL500 \,\mathrm{mL}. It selects an SRS of 2525 bottles without replacement from a lot of 10,00010{,}000. The sample mean is 501.2 mL501.2 \,\mathrm{mL} and the sample SD⁡\operatorname{SD} is 7.5 mL7.5 \,\mathrm{mL}. Previous process information supports an approximately normal population model. The population SD⁡\operatorname{SD} is unknown.

The target is μ\mu, the mean fill volume of all bottles in this lot. Because the question says “differs,” a change in either direction matters:

H0:μ=500 mLH_0:\mu=500\,\mathrm{mL}

Ha:μ≠500 mLH_a:\mu\ne500\,\mathrm{mL}

The method is a one-sample tt-test for a population mean. The sample average of 501.2 mL501.2 \,\mathrm{mL} does not change the hypotheses to a greater-than alternative. Direction comes from the question, before using the sample result to select it.

Pause and predict: Would a question about underfilling use the same alternative? What would change if the question specifically asked whether the mean is below 500 mL500 \,\mathrm{mL}?

Reveal the reasoning

The null stays H0:μ=500H_0:\mu=500. For evidence of underfilling, use Ha:μ<500H_a:\mu\lt500. The sample mean being above 500500 does not justify switching to a different question after seeing the data.

Key ideas and notation

μ\mu and μ0\mu_0

μ\mu is the unknown population mean. μ0\mu_0 is the benchmark mean specified under the null hypothesis.

For the bottles, μ0=500 mL\mu_0=500 \,\mathrm{mL}. The benchmark comes from the claim, not from xˉ\bar{x}.

xˉ\bar{x}, ss and nn

The sample mean, sample SD⁡\operatorname{SD} and sample size. These are observed summaries that later enter the test calculation.

xˉ=501.2 mL\bar{x}=501.2 \,\mathrm{mL}, s=7.5 mLs=7.5 \,\mathrm{mL} and n=25n=25. Sample SD⁡\operatorname{SD} ss does not make population SD⁡\operatorname{SD} σ\sigma known.

H0H_0: the null hypothesis

The reference claim used to build the test model. For this AP one-sample setup, write equality to a specified population mean.

H0:μ=500H_0:\mu=500. It does not say that every bottle contains exactly 500 mL500 \,\mathrm{mL}.

HaH_a: the alternative hypothesis

The population-level pattern the investigation seeks evidence for: less than, greater than or different from the benchmark.

Ha:μ≠500H_a:\mu\ne500 is two-sided. Ha:μ<500H_a:\mu\lt500 is one-sided.

μd\mu_d, dˉ\bar d and sds_d

The population mean, sample mean and sample SD⁡\operatorname{SD} of paired differences. Define dd before choosing the alternative.

For d=before−afterd=\text{before}-\text{after} time, μd>0\mu_d > 0 corresponds to lower after times on average.

Degrees of freedom

For a one-sample tt procedure, df=n−1\mathrm{df}=n-1. In a paired test, nn is the number of differences, which equals the number of complete pairs.

1010 students measured 2 times2\text{ times} supply 1010 differences, so df=9\mathrm{df}=9.

Choose the test and parameter

First decide whether you are studying a quantitative measurement or a categorical outcome. Then decide whether the observations form 11 sample, paired measurements or independent groups.

Visual guide 1: identify the structure before choosing a method
11 sample11 quantitative mean

Compare a population mean with a benchmark. Unknown σ\sigma → one-sample tt-test.

Matched pairs11 difference per pair

Subtract within each pair, then test the population mean of that difference list.

Another structureReconsider the method

Independent groups or a proportion need a different procedure.

22 measurements do not automatically mean 22 independent samples. The relationship between observations determines whether a paired analysis is appropriate.

Define a parameter in a full sentence: “μ\mu is the mean fill volume, in mL, of all bottles in this lot.” Include the mean, response measurement and target population.

For pairs: “μd\mu_d is the mean before-minus-after completion-time difference, in minutes, for students in this academy performing these comparable tasks.” Include the order of subtraction.

Keep methods distinct: A one-sample tt-test answers a mean question when σ\sigma is unknown. A proportion test answers a question about the fraction with a categorical outcome. A two-sample mean test handles 22 independent groups; it is developed later in Unit 4.

Large nn does not require switching an unknown-σ\sigma mean test to zz. A tt procedure still uses ss to estimate the unknown population SD⁡\operatorname{SD}. If σ\sigma were genuinely known, a different setup would be relevant; this topic focuses on unknown σ\sigma.

Write the hypotheses

State the null as H0:μ=μ0H_0:\mu=\mu_0. Translate the research question into one of the listed alternatives:

The question determines the alternative, before examining the sample result.
Question wordingAlternativeDirection
Lower, less, below, underHa:μ<μ0H_a:\mu\lt\mu_0One-sided, lower direction
Higher, more, above, exceedsHa:μ>μ0H_a:\mu\gt\mu_0One-sided, upper direction
Different, changed, not equalHa:μ≠μ0H_a:\mu\ne\mu_0Two-sided, either direction
Visual guide 2: 33 questions, 11 benchmark
Population mean hypothesis directions at one fixed benchmarkThree number-line diagrams share the null value of 500 milliliters. A less-than alternative points left, a greater-than alternative points right, and a not-equal alternative points both ways. These arrows show population-mean alternatives, not p-value areas. 495 500 505 Population mean fill volume (mL) H₀: μ = 500 Lower mean: Hₐ: μ < 500 495 500 505 Population mean fill volume (mL) H₀: μ = 500 Higher mean: Hₐ: μ > 500 495 500 505 Population mean fill volume (mL) H₀: μ = 500 Changed mean: Hₐ: μ ≠ 500 Population mean hypothesis directions at one fixed benchmarkThree number-line diagrams share the null value of 500 milliliters. A less-than alternative points left, a greater-than alternative points right, and a not-equal alternative points both ways. These arrows show population-mean alternatives, not p-value areas. 495 500 505 Population mean fill volume (mL) H₀: μ = 500 Lower mean: Hₐ: μ < 500 495
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