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Acceptance Sampling OC Curve

Plot the Operating Characteristic (OC) Curve for a sampling plan (n, c) to analyze producer's risk α and consumer's risk β.

Note:The OC Curve shows the probability of accepting a lot (y-axis) at each defect rate level (x-axis). A steeper curve indicates better discrimination.

Sampling plan: inspect 50 items; accept if defects ≤ 1 .

Use Case

A customer delivers a lot of 5,000 parts. QC must decide whether to accept the lot based on a statistical sampling plan without 100% inspection. Acceptance sampling balances the risk of accepting bad lots (consumer's risk β) against the risk of rejecting good lots (producer's risk α). This tool plots the OC (Operating Characteristic) curve, helping QA visualize acceptance probability at different actual defect rates and choose sampling plan parameters that best fit the quality requirements.

Example

Lot size N = 5,000. Sampling plan design: Sample size n = 80, acceptance number c = 2 (rejection number = 3) Key points on the OC curve: - p = 0% (perfect): acceptance probability Pa = 100% - p = 1% (AQL level): Pa = e^(-0.8) x (1 + 0.8 + 0.32) ≈ 89% (producer's risk α ≈ 11%) - p = 5%: Pa ≈ 22% - p = 10%: Pa ≈ 2% (very small consumer's risk β) → This plan discriminates well against high-defect-rate lots

Further reading: Statistical sampling plan design principles
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