Today I’m looking at some practical suggestions for reducing sample sizes for attribute testing. A sample is chosen to represent a population. The sample size should be sufficient to represent the population parameters such as mean and standard deviation. Here we’re looking at attribute testing, where a test results in either a pass or a fail.

The common way to select an appropriate sample size using reliability and confidence level is based on the success run theorem. The often-used sample sizes are shown below. The assumptions for using binomial distribution hold true here.

The formula for the success run theorem is given as:

n = ln(1 – C)/ ln(R), where n is the sample size, ln is the natural logarithm, C is the confidence level, and R is reliability.

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