Free tool

Audit materiality calculator

Choose a benchmark and your percentages. Then add the errors you found and see whether, together, they matter.

Starts on the worked questions’ invented client, Wrenhollow: $24m of profit before tax after a one-off $6m charge, or $480m of revenue. The percentages are judgments, not rules.

Results

Overall materiality
$1.50m
Performance materiality
$750k
Clearly trivial
$75k

Misstatements found

Each misstatement and its amount
$1.56m in total, above the clearly trivial line
PerformanceOverall

Material in total: the errors add up to more than overall materiality.

How audit materiality works

  1. Overall materiality. (Benchmark + one-off items added back) × the percentage you choose. Pick the figure the accounts’ users read most, usually profit for a company run for profit.
  2. Performance materiality. Overall materiality × a lower percentage, so that errors the audit doesn’t find are unlikely to add up to more than overall materiality. More risk, such as last year’s errors or a new system, means a lower percentage.
  3. Clearly trivial. Overall materiality × a small percentage. Errors below it are too small to record; everything above it is added up.
  4. Material in total. Add the recorded errors. A total at or above overall materiality is material even when every item is below it, and a small error can still be material by its nature.

The international standard, ISA 320, calls materiality a matter of professional judgment, shaped by what users of the accounts need. Its own example is 5% of profit before tax for a profit-oriented manufacturer, and it notes that a percentage of revenue is normally lower; the 1% of revenue it mentions is for a not-for-profit. It sets no fixed percentages, and each firm’s methodology is its own. The defaults here are the worked example’s choices: on Wrenhollow’s case, 5% of $30m gives $1.5m, performance materiality at 50% is $750k, and three errors below $1.5m add up to $1.56m, which is material.

Practice setting materiality aloud with an AI interviewer on Socratify.

Last reviewed Nov 9, 2026