Practical Data Analysis with JMP®

Confidence Intervals

In this first example, let's estimate the proportion of major pipeline disturbances in the United States in which natural gas actually ignites. We already know how to calculate the proportion of ignition incidents within our sample (about 0.81 or 81%), but that only describes the sample rather than the entire population. We shouldn't say that 81% of all disruptions involve fires, because that is overstating what we know. What we want to say is something like this: "Because our sample proportion was 0.81 and because we know a bit about sampling variability, we can comfortably conclude that the population proportion is ...."

We will use a confidence interval to express an inferential estimate. A confidence interval estimates ...