29:null 2a:["$","title",null,{"children":"Destructive pass/fail testing of batches | Kyle Huston | Observable"}] 2f:T418,html` In destructive sampling for lot testing of batches, the objective is to screen out flawed batches to produce a targeted level of batch reliability with some targeted confidence. However, if batches are not well-mixed, then the contamination might be present in a smaller fraction of samples. This means that more samples are required to increase the probability of screening a flawed batch.

The picture below shows the ${numSamplesPerBatch} samples for each of the ${numBatches} batches, totaling ${numSamplesPerBatch*numBatches} samples.

Flawless batches are represented by white rows.
In flawed batches, samples that are not detectably flawed are ${flawedBatchCleanSampleColor }, and samples that are detectably flawed are ${flawedBatchDirtySampleColor }.

If a row is entirely pink with no red samples, then this flawed batch would have not been screened by the test. `30:Tb18, function drawBatches() { var batchesAreFlawless = []; var samples = []; var i, j; for (i = 1; i <= numBatches; i++) { let batch = []; let batchIsFlawless = Math.random() > fracFlawedBatches; for (j = 1; j <= numSamplesPerBatch; j++) { if (batchIsFlawless || (Math.random() > fracBadSamplesInFlawedBatches)) { batch.push(0); } else { batch.push(1); } } batchesAreFlawless.push(batchIsFlawless); samples = samples.concat([batch]); }; var batchIdx = 0; var sampleIdx = 0; const indexedSamples = samples.map(batch => { const indexedBatch = batch.map(sample => { const indexedSample = { sample: sample, rowIdx: batchIdx, columnIdx: sampleIdx, batchIsFlawless: batchesAreFlawless[batchIdx], }; sampleIdx += 1; return indexedSample; }); batchIdx += 1; sampleIdx = 0; return indexedBatch; }).flat(); const gap = 5; const labelSpacing = 20; const size = Math.min((width - labelSpacing)/(numSamplesPerBatch + 2), (height - labelSpacing)/(numBatches + 2)) - gap; const columnWidth = size + gap; const rowHeight = size + gap; return html`` } 31:[] 2d:{"id":"da31f8fa7f268388","slug":"destructive-pass-fail-testing-of-batches","title":"Destructive pass/fail testing of batches","thumbnail":"49405ccbe89685f29ab9e2be922223432afdf83762ff7449b68a9477c9a96300","access_level":"public","create_time":"$D2020-11-24T16:57:54.054Z","update_time":"$D2022-03-04T17:07:31.565Z","view_time":null,"first_public_time":"$D2020-12-17T18:13:35.041Z","has_unpublished_edits":null,"fork":{"id":"50064204b82fdf22","slug":"pie-chart-component","title":"Pie chart component","owner":{"id":"863951e3ebe4c0ae","login":"d3","name":"D3"}},"stars":0,"starred":false,"owner":{"type":"team","id":"7757bfeb71a808f0","login":"khuston","name":"Kyle 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batches\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":518,"value":"viewof fracBadSamplesInFlawedBatches = slider({\n min: 0, \n max: 1, \n step: 0.01, \n format: \".0%\",\n title: \"ys\", \n description: \"Percentage of bad samples in flawed batches\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":725,"value":"viewof doAnimation = checkbox({\n options: [{ value: \"toggle\", label: \"Animate the picture\" }],\n value: \"toggle\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":775,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":463,"value":"{\n var drawing = drawBatches();\n for (const x 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