Frequentist Prediction Intervals
Frequentist prediction intervals are a statistical method used to estimate the range within which future observations from a population are likely to fall, based on a sample of data. They provide a measure of uncertainty for predictions, typically expressed as an interval with a specified confidence level (e.g., 95%). Unlike confidence intervals that estimate population parameters, prediction intervals account for both sampling error and the inherent variability of individual observations.
Developers should learn about frequentist prediction intervals when building predictive models, performing data analysis, or implementing statistical methods in applications such as forecasting, quality control, or risk assessment. They are particularly useful in scenarios where you need to quantify the uncertainty of future outcomes, such as predicting sales, estimating software defects, or assessing performance metrics in machine learning models.