Setting Up a Test for a Population Proportion
Before a test can answer a question, it needs a clear target and a justified plan. Learn to define the population proportion, write hypotheses in the right direction and check whether a one-proportion -test is appropriate.
By the end of this lesson, you should be able to:
- Define a population proportion using the population and response category.
- Choose a one-sample -test for a single population proportion.
- Write a null hypothesis and a one- or two-sided alternative.
- Choose the alternative from the research question before looking at the sample result.
- Check random sampling, the condition and expected counts under the null.
- Write a complete test setup with numerical justification.
Before you start: Know the difference between and and how random samples vary. Review Topic 3.2 for the sampling model, and Topic 3.3 for interval conditions.
First time learning this? Follow the school example from a research question to a complete setup.
Here to revise? Use the setup checklist, then attempt the practice before revealing answers.
The concept in 60 seconds
A school wants to investigate whether more than of all its students prefer an earlier lunch. Before collecting responses, it plans a simple random sample of students from students, without replacement. The eventual sample has yes responses.
The question concerns , the proportion of all students at this school who prefer earlier lunch. The sample proportion supplies evidence; is the unknown population value the test addresses.
The school investigates a population proportion above .
The population parameter is the hypothesis target.
Check whether the proposed one-proportion -test is appropriate.
The sample estimate provides evidence after the question has been set. It does not supply the null benchmark or choose the alternative direction.
supplies a benchmark model. states the direction the school is investigating. A test will ask whether the sample gives convincing evidence against that benchmark in the specified direction.
This lesson builds the setup: define , choose the procedure, write and , and justify the conditions. A sample estimate of alone is not a completed significance test.
All scenarios are fictional teaching examples. Charts show stated counts or an approximate theoretical null model, not collected school data.
Quick check: should the hypotheses use or ?
Use , the population proportion. is already known from the observed sample and provides evidence about .
Choose the parameter and method
A one-sample -test for a population proportion assesses a claim about the proportion in a specified category in population, when its conditions are met. Each response can be classified as success or failure. “Success” simply names the category being counted.
Define the parameter: “Let be the proportion of all students at this school who prefer an earlier lunch.”
A useful definition states the proportion, the population and the response category. “ is lunch preference” leaves the quantity and population unclear.
Proportion of all school students who prefer earlier lunch.
The specified population value assumed by .
yes responses divided by sampled students.
and describe different groups; is a fixed number in a hypothesis about . Keep all three roles separate.
| Symbol | Role | School value or meaning |
|---|---|---|
| Population proportion being tested | Unknown whole-school preference proportion | |
| Specified null value | ||
| Observed sample proportion | ||
| Observed success count | ||
| Sample size | ||
| Population size | ||
| Null hypothesis at the benchmark | ||
| Alternative hypothesis from the question |
Recognize a different target
- Average lunch duration: a mean is a different parameter and needs a mean procedure.
- Compare preference proportions at schools: a difference between population proportions needs a two-proportion procedure.
- Estimate a plausible range for proportion: a confidence interval addresses estimation.
- Assess a specified benchmark for proportion: a one-proportion test addresses the hypothesis-testing question here.
The word “percentage” is a clue to a proportion, but the research question and design determine the actual parameter. Write percentages as decimals in hypotheses: becomes .
Quick check: “ is the proportion of the sampled students who said yes.” What needs changing?
That describes . Define for all students at the school, the population of interest.
Understand the two hypotheses
The null hypothesis, , states the population value used as the starting model for the test. The alternative hypothesis, , states the departure for which evidence is being sought.
Choose the alternative required by the original research question:
is the numerical benchmark, such as . It is not a new unknown parameter and is not automatically the sample proportion. In the school example, while .
Why does the null use equality?
The test needs a specified population value to build its null sampling model. In AP Statistics, write the one-proportion test at the boundary of equality: .
For a question about “more than ,” the broader null situation can be expressed as . The usual AP setup tests at its boundary . This explains why equality appears even though the research question asks about an increase.
Assuming is not proving
We use as a model to evaluate evidence. We do not know at the setup stage whether it is true. Likewise, writing does not establish that it is true; it identifies what the investigation aims to detect.
A published claim can supply the benchmark in while a researcher’s question supplies . For example, “The provider says are satisfied; investigate whether satisfaction has fallen” gives and . Do not place a statement in just because it contains the word “claim.”
Quick check: is the usual alternative for an increase?
No. Use the strict alternative . Equality belongs in the null boundary, .
Choose the alternative direction
| Research wording | Alternative | Type |
|---|---|---|
| Greater, higher, increased, more than | One-sided: higher direction | |
| Less, lower, decreased, fewer than | One-sided: lower direction | |
| Different, changed, not equal to | Two-sided: either direction | |
| Improved | Depends on the counted category | Lower defects or higher nondefective proportion |
One-sided alternatives use or . A two-sided alternative uses and looks for a departure in either direction.