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Read It: Confidence Intervals
When we start making statistical inferences, we need means to represent the uncertainty in the results or the confidence that we, as the analysts, have in those results. For this, we turn to confidence intervals.
Definition: A confidence interval is an interval, computed from a sample, that has a predetermined change of capturing the value of the population parameter
There are some key aspects here that we can further define. The first is that confidence intervals are computed from a sample. Therefore, we are not computing confidence intervals on the population, but using the sample or sampling distribution. The next aspect is the emphasis on predetermined chance. This refers to the confidence level that you are using in your analysis. Essentially, this level specifies how confident are you that the true population parameter falls within a certain range. Often, you will see the 95th, or 95%, confidence interval, which is the most common interval to use. An example of this 95% confidence interval is shown below.
In the upcoming videos, we will demonstrate two methods for using bootstrapped sampling distributions to determine the 95% confidence interval.
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