For example, allowing 1 defect in the sample will require a sample size of 93 for the 95% reliability statement. If you want to make the same confidence statements while allowing 1 or more defects in your sample, the sample size required will be larger. Setting Producer’s Risk (α) at an arbitrary high value, such as 0.5 (note, α must be less than 1- β to run).īy changing RQL to 1%, the following C=0 plan can be obtained:.

Setting AQL at an arbitrary value lower than the RQL, such as 0.1%.Setting the Consumer’s Risk (β) at 0.05, which results in a 95% confidence level.Setting RQL at 5% for 95% reliability or 1% for 99% reliability.The same sample sizes can be generated using Stat > Quality Tools > Acceptance Sampling by Attributes by: These two sampling plans are really just C=0 Acceptance Sampling plans with an infinite lot size. Of course, if you don't feel like calculating this manually, you can use the Stat > Basic Statistics > 1 Proportion dialog box in Minitab to see the reliability levels for different sample sizes. , where the reliability is the probability of an in-spec item. Attribute SamplingĪ simple formula gives you the sample size required to make a 95% confidence statement about the probability an item will be in-spec when your sample of size n has zero defects. In this blog post, I'll focus on the attribute approach. The variables sampling approach has a strict normality assumption, but requires fewer samples. The attribute sampling approach is valid regardless of the underlying distribution of the data.

Variables Sampling: Determine the sample size for a continuous measurement that follows a Normal distribution.

Attribute Sampling: Determine the sample size for a categorical response that classifies each unit as Good or Bad (or, perhaps, In-spec or Out-of-spec).

The type of response will dictate whether you 'll use: The answer depends on the type of response variable you are using, categorical or continuous. How many samples do you need to be “95% confident that at least 95%-or even 99%-of your product is good?

Reliability of sample