# Estimating and Forecasting Biases in Projects - Part III

October 28, 2008 | Author: PM Hut | Filed under: Cost Estimating, Project Management Musings, Scheduling

Estimating and Forecasting Biases in Projects - Part III (#4 in the series Choosing the Wrong Portfolio of Projects)
By Miley W. Merkhofer

Base-Rate Bias

Base-rate bias refers to the tendency people have to ignore relevant statistic data when estimating likelihoods. For example, most people believe they are more likely to die from a terrorist attack than from colon cancer, even though statistics indicate otherwise. Variations of this bias are important in business environments, including the tendency people have to be insufficiently conservative (or “regressive”) when making predictions based on events that are partially random. Investors, for example, often expect a company that has just experienced record profits to earn as much or more the next year, even if there have been no changes in products or other elements of the business that would explain the recent, better-than-anticipated performance.

The tendency to underestimate the effort needed to complete a complex task has been attributed to base-rate bias. Instead of basing estimates mostly on the amount of time it has taken to do previous similar projects, managers typically take an “internal view” of the current project, thinking only about the tasks and scenarios leading to successful completion. This almost always leads to overly optimistic forecasts. One manager I know says he always multiplies the time his programmers say will be required to complete new software by a factor of two, because “that’s what usually happens.”

Small Sample and Conjunctive Bias

Small sample bias is another example of inaccurate statistical reasoning—people draw conclusions from a small sample of observations despite the fact that random variations mean that such samples have little real predictive power. Conjunctive events bias refers to the tendency for events that occur in conjunction with one another to make a result appear more likely. For example, the possibility that you may die during a vacation (due to any cause) must be more likely than the possibility that you will die on vacation as a result of a terrorist attack. Yet, one study showed that people are willing to pay more for an insurance policy that awards benefits in the event of death due to terrorism than one that awards benefits based on death due to any cause.

Miley W. (Lee) Merkhofer, Ph.D., is an author and practitioner in the field of decision analysis who specializes in assisting organizations in implementing project portfolio management. He has served on advisory panels for several government agencies and has received grants and research awards for work in the area. Lee is an editor of the journal Decision Analysis.

Prior to becoming an independent consultant, Lee was a Partner of PriceWaterhouseCoopers, where he founded that organization’s capital allocation and project prioritization business practice. Lee is a founding partner of Folio Technologies LLC, a provider of web-based, project portfolio management software.

Lee received his Ph.D. in engineering economic systems from Stanford University. He is the author of the book Decision Science and Social Risk Management and co-author of the book Risk Assessment Methods..

Additional papers on project portfolio management can be found on Lee’s website, www.prioritysystem.com. E-mail: lmerkhofer@prioritysystem.com.

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