Justifying a Claim Based on a Confidence Interval for a Population Proportion
A confidence interval gives a range of plausible values. Learn to explain that range, assess claims such as “,” and understand what confidence and precision really tell us.
By the end of this lesson, you should be able to:
- Interpret a confidence interval using the population and response category.
- Explain confidence level through repeated random sampling.
- Decide whether a proposed population proportion is plausible.
- Use the entire interval to assess a “greater than” or “less than” claim.
- Explain how confidence level and sample size affect margin of error and width.
- Write a conclusion that matches the evidence and the sampling design.
Before you start: Know and how a one-proportion interval is constructed. Review Topic 3.3 if you need the formula and condition checks.
First time learning this? Follow the school example, then compare a majority claim with a claim.
Here to revise? Use the interpretation checklist, then try the questions before opening the solutions.
The concept in 60 seconds
A school takes a simple random sample of students from its students, without replacement. Of the sampled students, prefer an earlier lunch. The sample proportion is .
After checking the conditions, a one-sample -interval gives an approximate confidence interval of for , the proportion of all students at this school who prefer an earlier lunch.
of students prefer earlier lunch.
A range of plausible values for the whole-school proportion.
Supports a majority. Leaves the stronger threshold uncertain.
A claim needs a comparison with the whole interval, not just the center. The school population proportion is unknown.
The school asks two questions:
- Does a majority prefer earlier lunch? Yes, the interval provides convincing evidence: every value in the interval is above .
- Do more than prefer it? The interval does not provide convincing evidence for that stronger claim: it includes values just below as well as above it.
The key habit: Compare the claim with the whole interval. The estimate alone does not tell you how much uncertainty remains.
Scenarios are fictional teaching examples. The repeated-sampling chart is a documented simulation; the other charts show calculations from stated sample values.
Quick check: does “ in the sample” prove “more than in the population”?
No. is sample result. The interval contains population proportions below , so this interval does not support that population claim convincingly.
Interpret the interval
A confidence interval estimates a population parameter. Here the parameter is a proportion, not an individual student’s response and not the already observed sample proportion.
Sentence pattern: “We are [confidence level] confident that the proportion of [population] who [response category] is between and .”
School interpretation: “We are confident that the proportion of all students at this school who prefer an earlier lunch is between and .”
You can say about instead. Both versions describe the same range on different scales. Include the confidence level, both endpoints, the population and the response category.
| Quantity | Meaning | School example |
|---|---|---|
| Fixed unknown population proportion | Proportion of all students who prefer earlier lunch | |
| Observed sample proportion | ||
| and | Lower and upper interval endpoints | About and at confidence |
| Confidence level | Long-run confidence level of the procedure | |
| or | A proposed value or comparison threshold | or , depending on the claim |
Keep the sample and population separate
The sample proportion is known: exactly for these responses. We do not need an interval to learn that value. The interval uses sample information to estimate the unknown proportion in the whole school.
“ of students prefer earlier lunch” is a different statement and is not supported by this interval. “ of student responses are between and ” also makes no sense: each student’s response is a category, such as yes or no.
Write for the actual target population: A sample from this school supports inference about this school when the sampling conditions are met. It does not automatically support a statement about all students in the country.
Quick check: repair “We are confident that lies between and .”
Replace with the population proportion , defined in context. is already known from the sample and is the center of this interval.
Explain confidence and coverage
Imagine repeatedly taking random samples of the same size from the same population and constructing an interval by the same method each time. The sample proportion changes, and the interval endpoints change. The population proportion stays fixed.
Under suitable conditions, a confidence procedure produces intervals that capture the true in about of repeated samples in the long run. Some intervals miss it.
Simulation: independent binomial samples, and