AP Statistics / Unit 3: Inference for Categorical Data: Proportions / Topic 3.6
NUM8ERS study notes · Topic 3.6

pp-Values

A sample result can differ from a claim even when the claim is true. Learn how a pp-value measures how unusual that result would be under the null model—and how to explain the probability clearly.

2026–27 curriculum6 worked examples10 practice questionsTail areas + simulation visuals

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

  • Explain a pp-value as a probability calculated assuming the null hypothesis is true.
  • Choose the correct extreme region from a one- or two-sided alternative.
  • Find a pp-value from a supplied zz-statistic or a null simulation.
  • Interpret the probability using the population claim and the observed sample result.
  • Explain why a smaller pp-value gives stronger evidence against the specified null model.
  • Avoid confusing a pp-value with the probability that a hypothesis is true.

Before you start: Know pp, p^\hat{p}, H0H_{0} and HaH_{a}. Topic 3.5 develops the hypothesis setup and conditions. Review Topic 3.2 if sampling distributions need a refresher.

First time learning this? Follow the school example, compare the 33 tail pictures, then try the first worked example.

Here to revise? Use the interpretation checklist, then attempt the practice before opening answers.

The concept in 60 seconds

A school investigates whether more than 60%60\% of its students prefer an earlier lunch. An SRS of 500500 students from 10,00010{,}000 students, taken without replacement, gives 320320 yes responses. The observed sample proportion is p^=320500=0.64\hat{p} =\frac{320}{500}= 0.64.

Let pp be the proportion of all students at this school who prefer an earlier lunch. The hypotheses are H0:p=0.60H_{0}: p = 0.60 and Ha:p>0.60H_{a}: p > 0.60. If the population really had 60%60\% support, different random samples would still give different proportions. The test asks how often the null model would give a result at least as high as 0.640.64.

Visual guide 1: the pp-value question in three steps
AssumeH0:p=0.60H_{0}: p = 0.60

Build the school’s null model at 60%60\% preference.

CompareObserved p^=0.64\hat{p} = 0.64

Ask about this high result and results even higher.

MeasureRight-tail area≈0.0339\text{Right-tail area} \approx 0.0339

For Ha:p>0.60H_{a}: p > 0.60, measure the null probability to the right.

The probability concerns possible sample results under H0H_{0}. It is not the probability that H0H_{0} itself is true.

A pp-value is the probability, assuming H0H_{0} is true, of a test statistic at least as extreme as the observed statistic in the direction specified by HaH_{a}.

For this school’s one-sided normal zz-test, the pp-value is approximately 0.03390.0339, or 3.39%3.39\%. Assuming the true preference proportion is 0.600.60, about 3.39%3.39\% of random samples of this size would produce a result this high or higher, according to the approximate null model.

That is a relatively unusual result under the 60%60\% model, providing evidence for a larger population proportion. It does not tell us the probability that the 60%60\% claim is true.

All contexts are fictional teaching examples. Normal tail probabilities are approximations for the one-proportion zz-test. The simulation later uses generated null-model statistics, not collected school responses.

Quick check: what do we assume when calculating the school’s pp-value?

Assume H0:p=0.60H_{0}: p = 0.60 is true, together with the justified sampling model. We evaluate sample results under that assumption; we do not calculate the probability that the assumption itself is true.

What is the probability about?

The phrase “assuming H0H_{0} is true” is essential. It tells us which model supplies the probability. The event is a sample test statistic in the extreme region determined by HaH_{a}.

p-value=P(statistic at least as extreme as observed∣H0 and the model assumptions)\text{p-value}=P(\text{statistic at least as extreme as observed}\mid H_0\text{ and the model assumptions})

The bar ∣\mid means “given” or “assuming.”

Read this in order: first assume the null model; then find the probability of the specified sample results. Reversing that order changes the question. A probability about data under a hypothesis is different from a probability about the hypothesis after observing data.

Visual guide 2: similar names, different roles
Population parameterpp is unknown

The actual whole-school preference proportion.

Null benchmarkp0=0.60p_{0} = 0.60

The fixed value used to build the null model.

Conditional probabilityp-value≈0.0339\text{p-value} \approx 0.0339

A tail probability under that model, for this observed result and alternative.

The sample statistic p^=0.64\hat{p} = 0.64 is the observed evidence. It differs from the population parameter, the null benchmark and the pp-value.

Read each symbol by its role in the school example.
Symbol or termMeaningSchool example
ppUnknown population proportionProportion of all school students preferring earlier lunch
p0p_{0}Null population benchmark0.600.60
p^=xn\hat{p} =\frac{x}{n}Observed sample proportion320500=0.64\frac{320}{500}= 0.64
zobsz_{\mathrm{obs}}Observed standardized test statisticApproximately +1.8257+1.8257
ZZRandom statistic under the null approximationStandard normal: center 00, SD 11
pp-valueNull probability of the specified extreme regionRight-tail probability≈0.0339\text{Right-tail probability} \approx 0.0339
BBNumber of simulated null statistics10,00010{,}000 in the illustrated run

Three different uses of “pp”

In this unit, pp alone usually names the unknown population proportion. p0p_{0} is the numerical proportion specified by H0H_{0}. The pp-value is a calculated probability about the test statistic. The words “pp-value” keep its role clear.

In the school example, pp is unknown, p0=0.60p_{0} = 0.60, p^=0.64\hat{p} = 0.64 and the right-tail pp-value is approximately 0.03390.0339. These 44 quantities answer different questions.

Do not reverse the condition: “Assuming 60%60\% of students prefer earlier lunch, how unusual is this sample?” is the question the pp-value answers. “Given this sample, what is the chance that exactly 60%60\% prefer earlier lunch?” needs a different framework and is not answered by this pp-value.

Quick check: does p-value=0.0339\text{p-value} = 0.0339 mean there is a 3.39%3.39\% chance that p=0.60p = 0.60?

No. The calculation assumes p=0.60p = 0.60. The 3.39%3.39\% is the approximate probability of a sample result as high as the observed result or higher under that assumption.

The null model and extreme results

A null distribution describes how the test statistic would vary across repeated samples if H0H_{0} were true. For a justified one-proportion zz-test, the standardized statistic is approximately standard normal under H0H_{0}: centered at 00 with standard deviation 11.

The school result corresponds to a supplied observed statistic of z≈+1.8257z \approx +1.8257. A positive zz means the observed proportion is above the null benchmark. A negative zz means it is below. Calculating zz from the sample is developed in Topic 3.7; here we use it to select and interpret probability areas.

What makes a result “as extreme or more extreme”?

  • For Ha:p>p0H_{a}: p > p_{0}: results at least as far toward larger proportions count. Use Z≥zobsZ \ge z_{\mathrm{obs}}.
  • For Ha:p<p0H_{a}: p < p_{0}: results at least as far toward smaller proportions count. Use Z≤zobsZ \le z_{\mathrm{obs}}.
  • For Ha:p≠p0H_{a}: p \ne p_{0}: departures at least as large in either direction count. For this symmetric normal model, use ∣Z∣≥∣zobs∣\lvert Z\rvert \ge \lvert z_{\mathrm{obs}}\rvert.

The pp-value includes a whole region of outcomes. It is not the probability of exactly the observed value. On a continuous normal model, 11 point has probability 00; a tail area can have positive probability.

Check that the reference model is appropriate

The school uses an SRS, satisfies 500≤0.10(10,000)=1,000500 \le 0.10(10{,}000) = 1{,}000, and has null expected counts 500(0.60)=300500(0.60) = 300 and 500(0.40)=200500(0.40) = 200, both at least 1010. These support the usual normal approximation.

A calculation cannot repair a biased sampling design or a poor probability model. If the normal approximation is unsuitable, a suitable exact or null simulation method may give a different probability. Do not treat a normal zz-test’s pp-value as an exact binomial probability.

Quick check: does the right-tail school pp-value include results above 0.640.64?

Yes. It includes 0.640.64 and higher in the approximate sampling model. “At least as extreme” includes more extreme results, not just the sample result we happened to observe.

Choose the correct tail area

Let ZZ follow the standard normal null approximation, and let zobsz_{\mathrm{obs}} be the observed statistic. The sign in HaH_{a} tells you which probability to calculate.

Use the alternative to choose the null probability region.
AlternativeExtreme region for ZZNormal-model calculation
Ha:p>p0H_{a}: p > p_{0}Z≥zobsZ \ge z_{\mathrm{obs}}Right area:1−Φ(zobs)\text{Right area}: 1 – \Phi (z_{\mathrm{obs}})
Ha:p<p0H_{a}: p < p_{0}Z≤zobsZ \le z_{\mathrm{obs}}Left area:Φ(zobs)\text{Left area}: \Phi (z_{\mathrm{obs}})
Ha:p≠p0H_{a}: p \ne p_{0}Z≤−∣zobs∣ or Z≥∣zobs∣Z\le-\lvert z_{\mathrm{obs}}\rvert\text{ or }Z\ge\lvert z_{\mathrm{obs}}\rvertBoth outer tails:2[1−Φ(∣zobs∣)]\text{Both outer tails}: 2[1 – \Phi (\lvert z_{\mathrm{obs}}\rvert )]
Visual guide 3: 11 observed result, 33 possible questions
Null-tail probabilities for three alternative hypothesesThree identical standard normal density curves with observed z approximately positive 1.8257. A greater-than alternative shades the right tail, p-value about 0.0339. A less-than alternative shades the large left region, p-value about 0.9661. A not-equal alternative shades both tails beyond negative and positive 1.8257, total p-value about 0.0679. 0.0 0.2 0.4 p-value ≈ 0.0339 +1.83 Hₐ: p > 0.60 0.0 0.2 0.4 p-value ≈ 0.9661 +1.83 Hₐ: p < 0.60
Posted on Google Google
0000003998 : Abdad Alam Shamim Alam profile picture
0000003998 : Abdad Alam Shamim Alam
Google star 1Google star 2Google star 3Google star 4Google star 5Trustindex verifies that the original source of the review is Google.
This is the bestest institution I have ever come to and I love it very much.
Posted on Google Google
Aliki S profile picture
Aliki S
Google star 1Google star 2Google star 3Google star 4Google star 5Trustindex verifies that the original source of the review is Google.
Posted on Google Google
Ali profile picture
Ali
Google star 1Google star 2Google star 3Google star 4Google star 5Trustindex verifies that the original source of the review is Google.
Sly is him
Posted on Google Google
Jitendra Kumar Kumawat profile picture
Jitendra Kumar Kumawat
Google star 1Google star 2Google star 3Google star 4Google star 5Trustindex verifies that the original source of the review is Google.
Posted on Google Google
Raahil Hasan profile picture
Raahil Hasan
Google star 1Google star 2Google star 3Google star 4Google star 5Trustindex verifies that the original source of the review is Google.
Posted on Google Google
Tina Mudarres profile picture
Tina Mudarres
Google star 1Google star 2Google star 3Google star 4Google star 5Trustindex verifies that the original source of the review is Google.
The classes are amazing and my child learnt so much
Posted on Google Google
alisha gadoya profile picture
alisha gadoya
Google star 1Google star 2Google star 3Google star 4Google star 5Trustindex verifies that the original source of the review is Google.
I have experienced a lot of good things, it has taught me so many things and from B/Cs I have gone to an A! This is wonderful and Mavish’s class is awesome.
Posted on Google Google
smasher 123 profile picture
smasher 123
Google star 1Google star 2Google star 3Google star 4Google star 5Trustindex verifies that the original source of the review is Google.
It’s sensational my kid went. First he only would get C now it’s all A’s really good and recommend
Posted on Google Google
Mosa Al- Samaraie profile picture
Mosa Al- Samaraie
Google star 1Google star 2Google star 3Google star 4Google star 5Trustindex verifies that the original source of the review is Google.
AMAZING PRICES GREAT TEACHERS STRAIGHT FORWARD LEARNING STEADY PACE IN TUTORING
Posted on Google Google
Cael Dagnelie profile picture
Cael Dagnelie
Google star 1Google star 2Google star 3Google star 4Google star 5Trustindex verifies that the original source of the review is Google.
I had a great experience learning math with Ms. Mavish. She explains complex topics in a very clear and simple way, which made it easier for me to understand and enjoy the subject. Her patience and dedication really stood out, and she always made sure that everyone in the class was keeping up. I especially appreciated how approachable she was — I never felt afraid to ask questions, and she was always willing to help. Thanks to her teaching, my confidence in math has grown a lot. I’m really thankful for the effort she puts into every lesson!

NUM8ERS is one of finest tutoring institutes in UAE, Located in Al Barsha 1, Dubai. Close to DUBAI AMERICAN ACADEMY (DAA) & AMERICAN SCHOOL OF DUBAI (ASD).