Reliability HotWire is a monthly eMagazine by ReliaSoft providing information and tips on how to best improve your reliability practices and get the most out of ReliaSoft’s tools for reliability and life data analysis. To help to understand the difference between the three types of statistical intervals and their applications, this article will discuss some basic assumptions of sample data.
What are tolerance and prediction intervals? How are they different from confidence intervals?
Confidence intervals are most commonly used. A confidence interval establishes an interval based on a sample that contains the true population (or process) parameter or metric x% of the time, if a random sample is drawn repeatedly from the same population.
Prediction intervals apply to the situation in which a statement needs to be made about a future population that does not currently exist. For example, samples are provided of a prototype and we need to know if future versions of the design will exhibit the same characteristic of interest as the prototype.
Tolerance intervals describe, for a sampled population (or process), an interval that contains a certain percentage of the population x% of the time. Note that for a normal distribution, a lower one-sided x% tolerance bound to be exceeded by y% of the population is equivalent to a lower one-sided x% confidence bound for the yth percentile; an upper one-sided x% tolerance bound to exceed y% of the population is equivalent to an upper one-sided x% confidence bound for the yth percentile.
For full article, view: Reliability Hotwire – Issue 194 April 2017