Title: Spreadsheet Errors: What We Know. What We Think We Can Do
ArXiv ID: 0802.3457
Date: 2008-03-10
Authors: - Dr. Raymond R. Panko (University of Hawaii)
📝 Abstract
Fifteen years of research studies have concluded unanimously that spreadsheet errors are both common and non-trivial. Now we must seek ways to reduce spreadsheet errors. Several approaches have been suggested, some of which are promising and others, while appealing because they are easy to do, are not likely to be effective. To date, only one technique, cell-by-cell code inspection, has been demonstrated to be effective. We need to conduct further research to determine the degree to which other techniques can reduce spreadsheet errors.
💡 Deep Analysis
Deep Dive into Spreadsheet Errors: What We Know. What We Think We Can Do.
Fifteen years of research studies have concluded unanimously that spreadsheet errors are both common and non-trivial. Now we must seek ways to reduce spreadsheet errors. Several approaches have been suggested, some of which are promising and others, while appealing because they are easy to do, are not likely to be effective. To date, only one technique, cell-by-cell code inspection, has been demonstrated to be effective. We need to conduct further research to determine the degree to which other techniques can reduce spreadsheet errors.
📄 Full Content
What We Know; What We Think
http://panko.cba.hawaii.edu/ssr/Mypapers%5CEUSPRIG_2000.htm
1 av 9
2005-01-19 14:43
Proceedings of the Spreadsheet Risk Symposium
European Spreadsheet Risks Interest Group (EuSpRIG)
Greenwich, England
July 17-18, 2000
Spreadsheet Errors:
What We Know.
What We Think We Can Do.
Abstract
Fifteen years of research studies have concluded unanimously that spreadsheet errors are both common and non-trivial. Now we
must seek ways to reduce spreadsheet errors. Several approaches have been suggested, some of which are promising and others,
while appealing because they are easy to do, are not likely to be effective. To date, only one technique, cell-by-cell code inspection,
has been demonstrated to be effective. We need to conduct further research to determine the degree to which other techniques can
reduce spreadsheet errors.
Introduction
Spreadsheets are widely used in organizations [McLean, Kappelman, & Thompson, 1993]. Each year, tens of millions
of managers and professionals around the world create hundreds of millions of spreadsheets.
Although many spreadsheets are small and simple throwaway calculations, surveys have shown that many
spreadsheets are quite large [Cale 1994, Cragg & King 1993, Floyd, Walls, & Marr 1995, Hall 1996]. Cragg and King
[1993] audited spreadsheets as large as 10,000 cells, and when Floyd, Walls, and Marr [1995] conducted a survey of 72
end user developers in four firms, asking subjects to select a single model, the average model had 6,000 cells.
Spreadsheets are also complex, using a large number of sophisticated functions [Hall, 1996].
Spreadsheets are also important. For instance, Gable, Yap, and Eng [1991] examined all 402 non-trivial
spreadsheets in one organization. Forty-six percent were rated as important or very important to the organization, and
59% of the spreadsheets were used at least monthly. In another study, Chan and Storey [1996] surveyed 256
spreadsheet developers. Each developer was asked to describe one of their spreadsheets. When asked to identify the
highest-level user of the spreadsheet’s data, 13% cited a vice president, and 42% cited their chief executive officer.
Under these circumstances, if many spreadsheets contain errors, the consequences could be dire. Unfortunately,
errors in bottom-line values are very likely because spreadsheet modeling is incredibly unforgiving of errors. A spelling
error in a word processing document will only occasionally create a material problem; but an error almost anywhere in
a spreadsheet will produce an incorrect bottom-line value. Unless the development error rate is close to one error in
every ten thousand cells, most large spreadsheets are likely to contain errors.
In fact, we know that humans are incapable of doing complex cognitive tasks with great accuracy. The Human
Error website [2000a] lists data from a number of studies of human cognitive errors. These studies indicate that even
for simple cognitive tasks, such as flipping switches, error rates are about one in 200. For more complex cognitive
tasks, such as writing lines of computer code, error rates are about one in 50 to one in 20.
Research on human error in many fields has shown that the problem is not sloppiness but rather fundamental
limitations in human cognition [Reason 1990]. Quite simply, we do not think the way we think we think. Human
cognition is built on complex mechanisms that inherently sacrifice some accuracy for speed. Speed in thinking was the
overriding evolutionary force for our hunter ancestors, and although occasional errors caused the loss of hunters, new
hunters were inexpensive and fun to make. Unfortunately, error rates acceptable in hunting and even in most computer
applications cannot be tolerated in spreadsheets. For spreadsheet accuracy, we must somehow overcome or at least
What We Know; What We Think
http://panko.cba.hawaii.edu/ssr/Mypapers%5CEUSPRIG_2000.htm
2 av 9
2005-01-19 14:43
reduce human cognitive accuracy limitations dramatically.
What We Know
Let us begin with what we actually know about spreadsheet errors and corporate practices to control spreadsheet errors.
More information about the studies listed in this section is available at the Spreadsheet Research website [Panko
2000b].
Spreadsheets Contain Errors
The Introduction noted that human error rates in complex cognitive tasks tend to be about 2% to 5%, so spreadsheet
errors must be fairly frequent or we will have to throw away decades of cognitive research.
Data from Field Studies
In fact, spreadsheet error rates actually are rather high. Table 1 shows data from seven field audits of real
organizational spreadsheets. The field audits found errors in 24% of the 367 spreadsheets audited, and most older audits
used audit techniques not likely to catch a majority of errors. The most recent field audits, in contrast, generally used
better methodologies and found