5 min read•january 3, 2023

Josh Argo

Jed Quiaoit

A confidence interval for a population proportion is based on sample data and is used to estimate the likely range of values for the population proportion. The confidence interval is constructed at a specific confidence level, which determines the probability that the interval contains the true population proportion.

Since each confidence interval is based on a random sample of data, the interval will either *contain* the population proportion or it will *not*. There is a specified probability, based on the confidence level, that the interval will contain the true population proportion. For example, if the confidence level is 95%, there is a 95% probability that the interval contains the true population proportion.

Let's return to our confidence interval that was given before:

We are estimating a 95% confidence interval of what proportion of high school math students pass their class. We were given a sample of 85 students where ~75% of them passed. We calculated a confidence interval for the **true population proportion **based off of our sample. The interval is given in the calculator output below: 🔢

We are given the 95% confidence interval (0.66125, 0.84463) as an estimate of the population proportion of high school students who are passing their math class.

In terms of what this means, it means we are 95% confident that the true population proportion of high school students who pass their math class is between 0.66125 and 0.84463. Notice that both of our endpoints are decimals less than 1. This is because we are estimating a proportion, which is **always** between 0 and 1. Anytime we are calculating any type of proportion, our answer should always be between 0 and 1.

Interpreting a confidence interval for a one-sample proportion should include a reference to the sample taken and details about the population it represents.

- "We are C%
*(90%, 95%, 99%, etc.)*confident that the confidence interval for a population proportion captures the population proportion of*(CONTEXT)*."

- "In repeated random sampling with the same sample size, approximately C% of confidence intervals created will capture the population proportion of
*(CONTEXT)*."

When interpreting a confidence interval for a population proportion, there are three things necessary to receive full credit: confidence level, context, and reference to **true population proportion. 🧠**

Our confidence level is generally given in the problem. This is the 95%, 90%, 98%, etc. This impacts the z* for our confidence interval and is necessary in including in our interpretation of the interval.

As with anything in AP Stats, **context** is essential to receive full credit. Anytime we write out an answer, we need to include it in context of the problem being asked. It is no different when interpreting a confidence interval. We need to ask ourselves, "What is this interval estimating?" and include that in our response.

We also need to be sure that our answer implies that we are estimating a population proportion, not just a sample proportion. After all, there's no reason to estimate something for our sample because we have the EXACT sample proportion as it was given to us. We are using that sample to estimate the **bigger picture** with our population. 😊

When we are given a population proportion that maybe we don't necessarily believe, we can use a confidence interval based off of a random sample to test that claim. The main way we are going to check the statistical claim is by seeing if the claimed population proportion is within our confidence interval. If it is in our confidence interval, then it is possible that the claim is true. If the claimed value is *not* in our interval, we may need to investigate further to see if the claim made by an article/study is in fact false. 🔎

In our example above dealing with students passing their math class, let's say that we recently read an article that said only 55% of all US students are passing their math class. Therefore, we took a random sample of 85 US math students and we were given the interval above: (0.66125, 0.84463).

Since 0.55 is *not* in our interval, we have reason to doubt the article that we read. We should definitely investigate it further. 🕵️

When we are given a claim that we are checking, our expected successes and failures change for our Large Counts Condition that we checked in Unit 6.2. Now that we are given a supposed proportion to be true for the population, we use that to calculate our expected successes and failures. So our large counts condition would change to 0.55(85) ≥ 10 & 0.45(85) ≥ 10, which still holds for this particular problem.

In other words, when we are given an actual p to check this condition, use it. When we aren't given a p-value, use the next best thing by using your p-hat. 🧢

🎥 **Watch: AP Stats - ****Inference: Confidence Intervals for Proportions**

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