Effect of Approximate Probability Distributions on Single and Double Acceptance Sampling Plans for Attributes
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Bibliographic record
Abstract
An acceptance sampling plan is a statement of the sample size to be used and the associated acceptance or rejection criteria for sentencing individual lots. An important measure of the performance of an acceptance sampling plan, such as the operating characteristic curve, is related to probability distributions. This research investigates the effect of binomial, Poisson and normal approximations to single and double acceptance sampling plans for attributes. For single-sampling plans, type-A OC curves show that the binomial approximation tends to overestimate the probability of acceptance Pa of the true hypergeometric distribution when the lot size is at most 10 times the sample size. The single-sampling plan with type-B OC curve displays that the Pa from Poisson is a slight overestimate of the true Pa for the binomial distribution with small n and large p, moreover, the Pa from normal approximation can be a significant underestimation, exact value, or overestimation of the binomial, even with small p. On double-sampling plans, the Poisson approximation results in a tiny overestimation, while the normal approximation appears to be a major underestimation of the binomial. In rectifying inspection, the characteristics of AOQL are very similar to the sampling plan.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it