5 Ways Auditors Can Overcome Confirmation Bias
Narrowly pursuing an investigation into what you initially suspect doesn’t just trip up scientists. Confirmation bias—one of five common judgment biases—has the potential to lead auditors up the wrong path just as easily.
In psychology and cognitive science, confirmation bias (or confirmatory bias) is a tendency to search for or interpret information in a way that confirms one’s preconceptions, leading to statistical errors.
Indeed, jumping to a conclusion is a particularly seductive line of reasoning during the early stages of an audit. At that time, financial information is often highly aggregated and minds trained to see patterns begin to see them. Yet quite often, the data are too ambiguous to allow the auditor to definitively identify the reason for a change in financial information.
This represents a trap, for the deeper one gets into investigating a particular hypothesis, the more difficult it becomes to consider contradictory ones. Rather, it’s common to seek evidence that supports suspicions and overlook data that don’t. Result: You’ve confirmed your bias—bypassing both the scientific method and best practices in auditing.
How to avoid this pitfall? Auditors can take five pragmatic steps to overcome this bias when performing analytical procedures. In fact, following these rules will improve decision making in other areas of the audit, as well:
Don’t Jump to Conclusions
Treat the initial data-gathering stage as a fact-finding mission, without trying to understand the specific causes of any identified fluctuations. That is, resist the temptation to immediately generate potential hypotheses. Wait until a more complete information set has been reviewed. Only then begin to consider reasons the data may differ from expectations.
Brainstorming: The Rule of Three
If possible, identify three potential causes for each unexpected data fluctuation that is identified. Why is three the magic number? Research has shown that auditors who develop three hypotheses are more likely to correctly identify misstatements when performing analytical procedures than those who develop just one hypothesis. From a probabilistic standpoint, the more plausible the expectations brainstormed, the higher the likelihood that the underlying cause of the fluctuation will be identified. However, developing too many initial hypotheses may constrain the auditor’s ability to efficiently evaluate each potential explanation. Research published in the
Journal of Accounting Research in 1999 revealed that auditors who develop three hypotheses are actually more efficient, and just as effective, at identifying misstatements through the use of analytical procedures as those who develop more than three hypotheses.
When identifying potential causes of a financial fluctuation, take note of the specific information that triggered a hypothesis. Present those data to a colleague to see whether he or she comes up with similar explanations. If the explanations are different, the colleague has assisted you in expanding your hypothesis set. If the explanations are similar, the colleague has provided you with some validation of your existing set. In other words, two minds can improve your chances of identifying the true explanation for the fluctuation.
Prove Yourself Wrong
It’s natural to seek out evidence that confirms these explanations once an initial set of hypotheses has been developed. However, accepting evidence as support ignores the fact that the same evidence could also indicate a different explanation. In a similar fashion, it’s also common to subconsciously ignore contradictory evidence. This is the heart of confirmation bias. Instead, try to disconfirm your initial suspicions by actively seeking out and weighing contradictory information. Such an approach can only lead to stronger and more definitive conclusions.
After identifying your initial hypotheses, the next required step is to investigate the data further to determine which (if any) is the actual cause of the data fluctuation. While performing this investigation, additional information will invariably be analyzed to confirm or disconfirm these explanations. Don’t forget to revisit old hypotheses and consider fresh ones when examining these new data. Remember that successful hypothesis generation during the performance of analytical procedures is an iterative process.
Suspicions may or may not be confirmed by using these five steps in an audit, but you’ll have certainty about two preconceptions: You’ll have avoided the pitfall of confirmation bias, and you’re far more likely to have discovered the true cause of financial fluctuations.
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