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CALCULATORS / ASSEMBLY, CAPACITY AND MANUFACTURING ECONOMICS

Inspection sample-size screening calculator

Binomial probability of seeing at least one defect under random independent sampling.

manufacturing-inspection-sampling.napkin / editable
✓ checks pass

SCENARIO COMPARISON

Compare the numbers.

This source describes an analytical model. Its quantities and checks are the view.

Scenariodetection
0.86738
0.800699

All calculated quantities are listed with the inputs. Switch to Source to inspect the equations.

SCENARIOS / CALCULATED VALUESSAME SOURCE

Change an input to see the consequences.

detection target0.87 ✓ passes

BASELINE INPUTS

CALCULATED

detection0.86738

Edits change the baseline source.

Calculated from your source · not saved
Baseline · Baseline
detection target0.87 ✓ passes
alternative · alternative
detection target0.80 ✓ passes

Illustrative first-order example. Checks are editable requirements, not design standards. The broader topic may require additional equations, data, and validation.

Formula and units

This example uses the following equations. Input names and units match the editable source above.

detection
detection = 1-(1-defectFraction)^sampleSize

Original worked example

These are the baseline values in the downloadable source. Edits above let you test your own values.

Given

defect fraction
0.02
sample size
100

Calculated

detection
0.86738

Assumptions and limits

Binomial probability of seeing at least one defect under random independent sampling.

The requirement checks use the limits written in this example. Passing a check does not establish that the model covers every load, material behavior, or operating condition in a real design.

Download this .napkin example ↓

Take it further with your agent

Napkin keeps the inputs, equations, checks and assumptions together in one study. Your agent sets it up; you change the numbers.

Try with Claude ↗

codex 'Use Napkin (https://napkin.engineering/mcp, or the local Napkin plugin) to build a study: Inspection sample-size screening. Inputs: lot assumptions; target error or detection probability; sample model. Outputs: sample size; stated statistical coverage. Model scope: Explicit process times, yield and costs supplied by the user. Read napkin_capabilities and the current snapshot. Ask me for the values that matter, label assumptions with units, author .napkin source, validate it and apply it with napkin_source using the current revision, then open the study. Add checks for the limits I supply. Keep unsupported analyses explicit.'

Claude needs the Napkin connector; Codex needs the Napkin plugin. Set up your agent ↗

The broader study

The topic brief below describes the broader scope. The worked example above states the particular calculation it implements.

  • Inputs: lot assumptions, target error or detection probability, sample model
  • Outputs: sample size, stated statistical coverage
  • Modes: calculate, compare, sweep
  • Drawing: Process graph and cost curves

EXECUTABLE WORKED EXAMPLE · FIRST-ORDER MODEL · CHECK THE SCOPE & ASSUMPTIONS

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