Interpretation of the 95% confidence interval in our example: In other words, there is a 95% chance of selecting a sample such that the 95% confidence interval calculated from that sample contains the true population mean.
About a 95% confidence interval for the mean, we can state that if we would repeat our sampling process infinitely, 95% of the constructed confidence intervals would contain the true population mean. We obtain the estimated mean (Est_mean), limits of the confidence interval (CI_Lower and CI_Upper) and limits of the prediction interval (PI_Lower and PI_Upper): (The R-code to do this is in the next paragraph) Est_meanĪ Confidence interval (CI) is an interval of good estimates of the unknown true population parameter. Let’s assume that we measure the whole blood hemoglobin concentration in a random sample of 100 persons. As you will see, prediction intervals (PI) resemble confidence intervals (CI), but the width of the PI is by definition larger than the width of the CI. Very often a confidence interval is misinterpreted as a prediction interval, leading to unrealistic “precise” predictions. Prediction interval versus Confidence interval But first, let's start with discussing the large difference between a confidence interval and a prediction interval. In the example, a new spectral method for measuring whole blood hemoglobin is compared with a reference method. In this post, I will illustrate the use of prediction intervals for the comparison of measurement methods.
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