Abstract
Before purchasing a large number of devices, a customer will likely ask the supplier about the defect level for the product being offered. The customer’s reliability inquiry is often expressed as: what is the defect level for the population of such devices in terms of number of defective devices per hundred, number of defective devices per thousand, number of defective devices per million (dpm), etc.? To determine the fraction defective, a sample of the devices is randomly selected from the population and this sample is tested/stressed to determine the fraction defective. After the fraction defective is determined for the sample, then it is only natural to ask: based on the sample size used, what is the confidence interval for the population fraction defective? To answer this critically important question, we must understand the basics of sampling theory.
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Notes
- 1.
Generally, in sampling theory, we round up the sample size requirement.
- 2.
- 3.
Note that this equation gives only single point estimation for the average failure rate over the interval t. It does not tell us whether the failure rate is increasing, decreasing, or remains constant. To determine this, several sequential periods of time would have to be studied.
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McPherson, J.W. (2013). Sampling Plans and Confidence Intervals. In: Reliability Physics and Engineering. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00122-7_17
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DOI: https://doi.org/10.1007/978-3-319-00122-7_17
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