ToolNestr

AQL Sampling & Lead Time Estimator

Enter your lot size to find the standard sample size and accept/reject numbers for a quality inspection.

Reviewed by the ToolNestr Editorial Team — July 2026

How AQL sample size is determined

ISO 2859-1 (equivalent to ANSI/ASQ Z1.4) links lot size to a sample size using published tables rather than a simple percentage — larger lots use a proportionally smaller sampling fraction, since statistical confidence scales efficiently with sample size beyond a certain point. This calculator uses General Inspection Level II, the standard default severity for routine apparel shipment inspections, at Normal inspection tightness.

Once the sample size is known, the accept (Ac) and reject (Re) numbers come from a second table cross-referencing sample size against the chosen AQL — the maximum acceptable defect rate. This tool uses 2.5% AQL for major defects, the most widely used standard in apparel quality control, covering issues like stitching defects, seam slippage, or measurement out-of-tolerance that affect the garment's fitness for sale but aren't necessarily safety-critical.

Sample sizes and Ac/Re numbers follow ISO 2859-1 (ANSI/ASQ Z1.4) General Inspection Level II, Normal severity, AQL 2.5%. Always confirm against the full published standard or your QC provider for contractual inspections.

Worked example

A shipment of 5,000 units needs inspection before release.

Lot size: 5,000 (falls in 3,201-10,000 range)
Sample size: 200 units
Accept: 10 or fewer major defects
Reject: 11 or more major defects

Real-world context

Third-party quality-control agencies performing pre-shipment inspections for apparel brands almost universally reference ISO 2859-1 / ANSI Z1.4 tables like this one, since they give both the brand and the factory a shared, objective, industry-standard basis for a pass/fail decision — rather than a subjective judgment call by whoever happens to be doing the inspection.

Brands often run separate, stricter AQL levels for critical and minor defects alongside the major-defect AQL shown here — a full inspection report typically distinguishes critical defects (safety issues, near-zero tolerance), major defects (this 2.5% AQL), and minor defects (a higher, more lenient AQL, often 4.0%), each with its own Ac/Re numbers.

Common misconceptions

"AQL sampling means checking a fixed 10% of every order." Not a flat percentage. Sample size is drawn from published lot-size-based tables, not a simple percentage of the lot — larger lots use a smaller sampling fraction, not a constant one.

"Passing AQL inspection guarantees zero defects in the shipment." It doesn't — that's the whole point of sampling. AQL inspection accepts a lot with a statistically bounded, known level of defect risk; it's a quality-control tool for large-scale production, not a zero-defect guarantee.

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Frequently asked questions

What is AQL?

AQL (Acceptable Quality Limit) is the maximum percentage of defective units in a batch that's still considered acceptable for a given inspection plan. AQL 2.5% for major defects is the most common standard used in apparel quality control, defined by the international ISO 2859-1 sampling standard (equivalent to ANSI/ASQ Z1.4).

Why don't I need to inspect every unit?

Statistical sampling lets an inspector check a much smaller, randomly-drawn sample and still draw a reliable conclusion about the whole lot's quality, at a small, known risk level — inspecting 100% of a large order is rarely practical or cost-effective for routine QC.

What do the accept (Ac) and reject (Re) numbers mean?

Ac is the maximum number of defective units found in the sample that still allows the lot to pass; Re is the number of defects at which the lot is rejected. Finding a defect count between Ac and Re (exclusive) — i.e. exactly Re defects or more — fails the lot.

What sampling severity level does this use?

General Inspection Level II, Normal severity — the standard default used for most routine apparel shipments. Tightened or reduced inspection levels exist for suppliers with a recent history of failures or consistent passes, respectively, and use different sample sizes.

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