June 15, 1999
FOR IMMEDIATE RELEASE
Sampling for Sales and Use Tax Audits.
Winning Essay, Sales Tax Literary Award Competition, Institute for
Professionals in Taxation, Awarded June 15, 1999, by Dr. Will Yancey
Introduction
Why Sample?
Sampling Risk, Nonsampling Risk and Sampling Cost
Nonstatistical Versus Statistical Sampling
Financial Statement Versus Transaction Tax Audits
Summary and Call for Education
Introduction
Sampling is growing as an important issue in sales and use tax audits. Auditors
from some state tax departments are implementing more advanced sampling
methods than block sampling that has been used so heavily in the past.
Taxpayers are more carefully analyzing audit sampling plans to verify
the accuracy and efficiency of those plans.
The purpose of this essay is to describe some fundamental concepts of sampling
in sales and use tax audits. First, the essay answers the question, "Why
sample instead of detail examination of each transaction?" Answering
this question leads to the second topic, the definitions of sampling risk,
nonsampling risk, and sampling cost. Third, nonstatistical and statistical
sampling methods are compared. Fourth, differences between financial statement
audits and transaction tax audits are explained. The final section provides
a summary and call for more education on sampling for sales and use tax
professionals.
Why Sample?
Sampling is necessary when the taxpayer has so many records that a detailed examination
of each record is not possible. Virtually all state sales and use tax
auditors use some form of sampling on large corporate taxpayers. Without
sampling the auditors would be unable to complete audits in a reasonable
amount of time.
Sales and use tax audit samples consist of a subset of records drawn from a
large population of transaction records. These sample transactions are
examined for errors, including tax underpayments and overpayments. Projections
are made from the errors observed in the sample to an estimate of the
errors in the population. Usually positive and negative errors from the
sample transactions are netted against each other to yield a total net
error. Some audits consist of detail examination of all transactions above
some dollar threshold, and sampling for transactions below that threshold.
Taxpayers benefit from efficient and effective sampling in sales and use tax audits.
Proper sampling improves efficiency by testing a much smaller number of
records than a complete detail examination of the population. The sampling
plan is effective when it provides an accurate estimate of the true amount
of error in the population. Effectiveness is not directly measurable,
since the true amount of error would be known only if every record were
examined in detail.
Sampling Risk, Nonsampling Risk, and Sampling Cost
Researchers in many scientific disciplines have evolved sophisticated sampling methods
over the past century. Some concepts relevant to sales and use tax audits
are sampling risk, nonsampling risk, and sampling costs.
Sampling risk
is the chance that the estimate projected from the sample is significantly
different from the amount that would be determined if every item in the
population were tested. Sampling risk is inevitable whenever a sample
is tested rather than the complete population. Good sampling procedures
are designed to reduce sampling risk, but this risk cannot be eliminated
without testing every item in the population. Unless we test every item
in the population, we do not know the difference between the amount projected
from the sample and the true amount in the population.
Sampling
risk is increased when small samples are taken. For example, suppose we
know that in a population of 1,000,000 records approximately two percent
of those records contain tax errors. Taking a sample of only 50 records
from this population has a high sampling risk, because there is a high
chance that this sample will show zero errors. It is remotely possible
that a sample of 50 will include exactly one error and that the projection
from that one error will be close to the true amount of underpaid or overpaid
tax in the population. Taking a sample of 500 items increases the chances
of accurately estimating the error rate (10 errors in a sample of 500
gives a two percent error rate).
Sampling
risk should not be confused with sampling
bias. Sampling
risk is the probability that the sample projection differs from the population
without specifying a direction. Sampling bias is the probable
direction
of the difference between the estimate and the population. The government
will probably collect more revenue than it should when the sample is biased
towards over assessment. The taxpayer probably pays less than it should
when the sample bias is towards under assessment. People experienced with
sampling in the sales and use tax environment can evaluate an audit sampling
plan for both risk and bias.
Nonsampling risk
is the risk that an incorrect determination of total error would be made
even if the testing procedures were applied to every item in the population.
An example of nonsampling risk is where the auditor incorrectly applies
the law to determine the taxability of items in the sample. Another example
of nonsampling risk is when reversing entries are omitted from the sample,
but the projection is made over a population that includes reversing entries.
Sampling cost
in a sales and use tax audit is the total cost of planning, selecting,
testing, and reviewing the sample. The most obvious costs are the time
for clerical staff to find and copy documents, and for the auditor to
examine those documents. Sampling costs also include the time and expense
for the taxpayer and its representatives to review the auditor’s
work and to resolve difficult items in the sample.
Proper sample planning must consider the trade-offs between sampling risk, nonsampling
risk, and sampling cost. Decreasing sampling risk often requires increasing
sample size and sampling cost. Decreasing nonsampling risk may require
more extensive training and review that increases sampling cost. However,
sampling risk and nonsampling risk are also costly in the sense that incorrect
assessments or refunds. The taxpayer and the auditor should become aware
of the risks and costs, and reach some agreement on how to trade-off those
risks and costs.
Nonstatistical Versus Statistical Sampling
In
nonstatistical sampling,
the auditors estimate sampling risk by relying on professional judgment.
The severe limitation of nonstatistical sampling is that it does not allow
the auditor to make a quantitative estimate of sampling risk. An example
of nonstatistical sampling is block sampling where auditors select a few
days, weeks, or months from the population. The auditor assumes the sample
time periods are representative of the entire population. By not taking
sample transactions over the entire audit period, block samples increase
sampling risk. If the tax error rate in the sample time periods differs
significantly from the time periods not sampled, the block sampling method
will produce results that are not valid.
Statistical sampling methods
do provide quantitative estimates of sampling risk. Statistical sampling
requires that the person selecting the sample relies on a random sample
selection process rather than his or her judgment about the extent to
which the sample represents the population. The projected error from a
statistical sample may differ significantly from the true error in the
population, but this sampling risk can be quantified using statistical
formulas derived from the theory of probability. Sampling risk is usually
expressed as a confidence interval, such as a 95 percent chance that the
total error is between $400,000 and $480,000.
A statistical sampling plan begins with (1) a goal for accuracy, such as a 95 percent
confidence interval, (2) a tolerable error, such as $25,000, and (3) an
estimate of the error rate in the population, such as one percent. Statistical
formulas are used to compute the sample size that is likely to achieve
these goals. The population is divided into two or more strata, and a
specified number of items are randomly selected from each stratum. After
the sample results are collected, the sample is evaluated to determine
if the sampling goals are achieved. If those goals are not achieved, the
sample could be expanded or the goals could be modified.
One nonstatistical method of sample evaluation used by some auditors is to
compare the distribution of invoice dollars in the sample to the distribution
of invoice dollars in the population from which the sample is drawn. If
the sample’s mean dollars per transaction is close to the population’s
mean, the auditor concludes the sample is representative of the population.
The auditor assumes that if the invoice dollars are representative, then
the projected error from the sample will also be representative of the
population. This method relies on the auditor’s judgment rather than
a quantified estimate of sampling risk.
Financial Statement Versus Transaction Tax Audits
The primary purpose of a financial statement audit is to determine whether
the financial statements taken as whole are materially correct. Financial
auditors explicitly consider materiality. For example, the materiality
threshold for a corporation with $10 billion in assets might be $50 million.
The financial auditors’ null hypothesis is that the financial statements
are materially correct. Sample data is gathered to determine whether there
is sufficient evidence to reject the null hypothesis in favor of the alternative
hypothesis that the financial statements are materially incorrect. Similarly,
financial auditors also test a corporation’s system of internal controls
to determine whether they are or are not functioning properly. Thus, the
primary purpose of financial statement auditors is reach a conclusion
about whether to accept or reject a null hypothesis. A secondary purpose
of financial audits is to estimate the amount of adjustment that would
materially correct the financial statements.
In contrast, the primary purpose of a tax audit is to estimate the amount
of overpaid or underpaid tax. The tax auditor may have no specific policy
on the materiality threshold for a sales and use tax audit. If the threshold
is zero, then any adjustment including one single dollar is material.
When a materiality threshold is specified for tax audits, it is far less
than the level chosen for the audit of taxpayer’s consolidated financial
statements. A possible secondary purpose of a tax audit is to accept or
reject the hypothesis that the taxpayer’s tax accrual system is functioning
with an acceptably low error rate.
Financial statement auditors have the advantage of well-developed set of professional
standards on audit sampling. The best-known financial statement audit
standard is the American Institute of Certified Public Accountants’
Statement on Auditing Standards Number 39 ("SAS 39") that describes
both statistical and nonstatistical sampling. SAS 39 provides general
guidance for auditors to consider in planning an financial statement audit.
Professional standards for sales and use tax audits do not exist at this
time.
Summary And Call For Education
The preceding sections of this essay describe some general concepts for sampling
in sales and use tax audits. Sampling is needed when the taxpayer’s
records are too voluminous to examine in detail. Sample planning requires
trade-offs between sampling risk, nonsampling risk, and sampling cost.
Statistical sampling methods provide a quantified estimate of sampling
risk, and nonstatistical methods do not. Financial statement audits have
a higher materiality threshold and more emphasis on hypothesis testing
than sales and use tax audits.
The reluctance of auditors, taxpayers, and consultants to discuss sampling
issues is due to a lack of understanding of the fundamental concepts.
Better education and training guides specifically applying sampling to
practical problems in sales and use tax audits will improve understanding.
Cooperation between government agencies and professional associations
will improve the implementation and training for sales and use tax audit
sampling. As all parties become better educated, they will become more
confident in the use of sampling. Better cooperation might decrease sampling
costs and audit dispute resolution time.
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