![]() This example is common in manufacturing, except you might be inspecting parts in a large shipment, to see if the shipment should be accepted. If you have 107 applications, but don’t have time to check all of them individually, you could take a sample of them (n=33) and perform analysis on the sample to predict the results of all 107 applications. Let’s say we are reviewing applications for a job opening at a nonprofit, and you want to inspect the applications to see which ones are actually coming from “local” candidates (within 20 miles of the facility, which was part of your requirements). But what if you want to calculate a confidence interval to understand how good or bad it is within the population? If you are inspecting a sample of items, and there are some defects or errors, you can easily calculate the defect rate by taking the number of defects divided by the number of samples. A common question I get asked is: how accurate are my defect rate predictions? ![]()
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