Data considerations for Tolerance Intervals (Normal Distribution)

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

The data must be continuous
Continuous data are measurements that may potentially be any numeric value within a range of values along a continuous scale, including fractional or decimal values. Common examples include measurements such as length, weight, and temperature.
The data must follow a normal distribution to use the results from the normal method
If your data follow a normal distribution, then the normal method is more precise and economical than the nonparametric method. The normal method allows you to achieve smaller margins of error, even when you have fewer observations.
The normal method is not robust to severe departures from normality. Use the normal method only if you know that your population is normally distributed. If you are unsure whether the population follows a normal distribution, or if you know that the population does not follow a normal distribution, then use the nonparametric method.
Collect enough data for the nonparametric method
The nonparametric method usually requires larger sample sizes than the normal method. You must use a relatively large sample size, approximately 90 or more for the tolerance interval to be accurate. To have an accurate tolerance interval, the achieved confidence level must be close to your target confidence level. If your sample size is not large enough, the achieved confidence level for your tolerance interval might be much lower than the target confidence level, and therefore, might produce inaccurate results.
To determine an appropriate sample size for a tolerance interval that meets your accuracy and precision objectives, go to Overview for Sample Size for Tolerance Intervals.
By using this site you agree to the use of cookies for analytics and personalized content.  Read our policy