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7: Confidence Intervals

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    58876
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    Chapter 7: Confidence Intervals

    Imagine you’re trying to estimate the average rent for a one-bedroom apartment in your city. You can’t look at every apartment listing in the entire market, so you take a sample say, 50 listings and calculate the average rent from just those. But here’s the big question:

    How close is your sample mean to the real population mean?

    This is where confidence intervals come in. Instead of giving a single number as an estimate, a confidence interval gives a range one that likely contains the true population value. It’s one of the most important ideas in modern statistics, because it helps us make informed guesses while acknowledging uncertainty.

    Key idea: A confidence interval is built around your sample statistic.

    It takes the form: estimate ± margin of error

    Confidence intervals pop up everywhere: opinion polls, clinical studies, economic estimates, even weather forecasts. Instead of saying “we know the exact truth,” we say something more honest (and useful): “We’re this confident that the truth lies somewhere in here.”

    In this chapter, we’ll explore:

    • How to use a sample to estimate a population parameter
    • The structure and interpretation of a confidence interval
    • How to find critical values (like \(z*\) and \(t*\))
    • The difference between intervals for proportions and for means
    • How sample size and confidence level affect the accuracy and width of intervals

    This page titled 7: Confidence Intervals is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Mathematics Department.

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