Determine Success Testing Sample Size

Determine Success Testing Sample Size

“How many samples do we need?” is a very common question. It is one you will receive when planning nearly any kind of reliability testing. It is a great question.

Having too few samples means the results are likely not useful to make a decision. Too many samples improve the results, yet does add unnecessary costs. Getting the right sample size is an exercise starting in statistics and ending with a balance of constraints.

There are six elements to consider when estimating sample size. We will use the success testing formula, a life test with no planned failures, to outline the necessary considerations.

Statistical Considerations

The variance of the population is rarely known when contemplating sample sizes. It is important to know the value or to at least have a reasonable estimate. The more variability, larger the variance, the more samples you will need for any sample size.

When the population has a larger amount of variability, the ability to detect or measure a statistic (mean, standard deviation, etc.) accurately becomes more difficult than from a population with a smaller variance.