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Graduate-Level Modeling · Audit & Assurance

Audit Sampling & Monetary Unit Sampling (MUS) Calculator

Sample size tables in audit textbooks look like they were handed down from on high. They weren't, they come straight out of the Poisson distribution, and this tool solves them live instead of looking them up.

How To Use This Model

Reading This Tool

Plan a Monetary Unit Sample from your population value, tolerable misstatement, and confidence level, then evaluate the results once you've actually tested the sample.

Reliability factors aren't pulled from a static table, they're solved live from the Poisson distribution, the same mathematics that generates the published AICPA tables, so you can see exactly where those textbook numbers come from.

Sample Planning Inputs

Sample Test Results (Evaluation)

Tainting is the misstatement amount divided by the recorded (book) amount of the sampled item, a fully fictitious item has 100% tainting, an item overstated by half its value has 50%.

Sample Plan

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Required Sample Size

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Sampling Interval

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Upper Misstatement Limit

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Tolerable Misstatement

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Upper Misstatement Limit Build-Up

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Why Expected Misstatements Expand The Sample So Fast

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What The Incremental Allowance Is Actually Buying You

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Reading A Failed Evaluation

If the Upper Misstatement Limit exceeds tolerable misstatement, that does not automatically mean the population is materially misstated, it means the sample evidence alone can't support concluding it isn't. The usual next steps are extending the sample, investigating the root cause of the misstatements found, or asking the client to book an adjustment that brings the projected misstatement down.

Required Sample Size vs. Expected Misstatements (Same Population & Confidence)

Reliability Factors Solved At Your Confidence Level

Expected/Found Misstatements (k)Reliability Factor RF(k)
Audit Sampling & Substantive Testing

The Core Formulas

RF(C, k) solves: Σi=0..k e−λλi/i! = 1 − C, RF = λ
Sample Size n = ⌈ Population Value × RF(C, kexpected) / Tolerable Misstatement ⌉
Sampling Interval SI = Population Value / n
UEL = SI·RF(C,0) + SI·Σtaintj + SI·Σ[RF(C,j)−RF(C,j−1)−1]·taintj

A reliability factor is the value that makes the probability of observing k or fewer misstatements, if the true rate were exactly the risk of incorrect acceptance, equal to that risk. Solving this Poisson equation directly (via bisection) reproduces the published AICPA reliability factor tables to three decimal places, since that's how those tables were built in the first place.

Monetary Unit (PPS) Sampling In One Paragraph

Monetary Unit Sampling treats every individual dollar in the population as a sampling unit, which automatically weights larger balances proportionally more likely to be selected, exactly where misstatement dollars are more likely to live. The Upper Misstatement Limit combines three pieces: basic precision (the sampling risk cushion if zero misstatements were found), the projected misstatement (extrapolating what was found across the whole population via the sampling interval), and an incremental allowance that grows with each additional misstatement found, reflecting the added uncertainty about the true error rate.

When To Actually Use This Method

  • Substantive testing of accounts receivable, inventory, or other populations that can be misstated in one direction (overstatement), MUS is not well suited to populations prone to understatement or containing many zero or negative balances.
  • Teaching the statistical foundation behind audit sampling standards in an assurance or auditing course.
  • Documenting a defensible, quantitative basis for a sample size and an evaluation conclusion in a working paper.

Key Assumptions & Limitations

  • This tool uses a single average tainting percentage applied uniformly across all misstatements found, real practice ranks individual taintings from highest to lowest before applying the incremental allowance factors.
  • MUS assumes overstatement-only errors, understatements require a separate testing approach (classical variables sampling or a targeted search).
  • A population with many balances below the sampling interval risks under-selecting small accounts entirely, supplemental testing of that stratum is often warranted.

Foundational References

Leslie, D. A., Teitlebaum, A. D., & Anderson, R. J. (1979). Dollar-Unit Sampling: A Practical Guide for Auditors. Copp Clark Pitman.

American Institute of Certified Public Accountants. Audit Sampling (AICPA Audit Guide).

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