|
|
|
People are notoriously poor at estimating and forecasting. They interpret statistical correlation as implying cause-and-effect. They tend to naively extrapolate trends that they perceive in charts. They draw inferences from samples that are too small or unrepresentative. They routinely overestimate their abilities and underestimate the time and effort required to complete difficult tasks. Estimating and forecasting biases are a special class of biases important to project-selection decision making. Misestimating LikelihoodsUncertain situations are particularly troublesome. Studies show that people make systematic errors when estimating how likely uncertain events are. As shown in Figure 2, likely outcomes are typically estimated to be less probable than they really are. And, outcomes that are quite unlikely are typically estimated to be more probable than they are. Furthermore, people often behave as if extremely unlikely, but still possible, outcomes have no chance whatsoever of occurring. |
||
 Figure 2: People systematically over- or under-estimate probabilities. OverconfidenceOverconfidence is another powerful bias. We believe we are more accurate at making estimates than we are. I've often repeated a well-known demonstration to illustrate what I call the "2/50 rule." People are asked to provide confidence intervals within which they are "98% sure" that various uncertain quantities lie. The quantities for the questions are selected from an Almanac, for example, "What's the elevation of the highest mountain in Texas?" "Give me low and high values within which you are 98% sure that the actual value falls." When the true value is checked, up to 50% of the time it falls outside of the specified confidence intervals. If people were not overconfident, values outside their 98% confidence ranges would occur only 2% of the time. 
Titanic -- The ship that couldn't sink Overconfidence is also demonstrated by the many examples of people expressing confidence about things that are subsequently proved wrong. For example, British Mathematician Lord Kelvin said, "Heavier-than-air flying machines are impossible." Thomas Watson, founding Chairman of IBM, reportedly said, "I think there is a world market for about five computers." The Titanic was the ship that couldn't sink. Likewise, surveys show that most drivers report that they are better than average, and most companies believe their brands to be of "above-average" value. AnchoringA related bias is anchoring. Initial impressions become reference points that anchor subsequent thoughts and judgments. For example, if a salesperson attempts to forecast next year sales by looking at sales in the previous year, the old numbers become anchors, which the salesperson then adjusts based on other factors. The adjustment is usually insufficient. 
Minimum payment anchor Anchors can be set through any mechanism that creates a reference point. For example, in one study, groups of consumers were shown credit card bills that either did or did not contain minimum payment requirements and asked how they would pay the debt off given their real-life finances. The payments for those who indicated they would pay over time were 70% lower for the group who saw information on minimum payments compared to those who did not. Apparently, the minimum payment works as an anchor, causing the card holder to pay a smaller amount than would have been paid in the absence of the anchor. Recent events are easy to recall and often become anchors. Thus, investors tend to believe that what's happening currently to the price of a stock will continue to happen into the future (thus, anchoring contributes to stock price volatility since it prolongs up- and downswings) Knowing that recent job performance has a more pronounced affect on impressions, workers naturally give more attention to performance in the 3 months just prior to reviews than in the previous nine months. Motivational BiasesMotivational biases can affect estimates and forecasts whenever estimators believe that the quantities expressed may affect them personally. For example, managers may have an incentive to overstate productivity forecasts to reduce the risk that the capital dollars allocated to their business units will be reduced. More subtle biases also affect estimates provided from managers, and the effect can depend on the individual. For example, project managers who are anxious to be perceived as successful may pad cost and schedule estimates to reduce the likelihood that they fail to achieve expectations. On the other hand, project managers who want to be regarded (consciously or unconsciously) as high-performers may underestimate the required work and set unrealistic goals. Most managers are overly optimistic. When companies collect data on the financial returns from projects, they almost always find that actual returns are well-below forecasted returns. Motivational biases can also cause experts to minimize the uncertainty associated with the estimates that they provide. They may feel that someone in their position is expected to know, with high certainty, what is likely to happen within the domain of their expertise. Likewise, I have found that managers sometimes become defensive when asked to estimate the potential losses associated with a proposed project, even in environments where it is well-known that projects can fail. They may feel that admitting to downside potential would suggest deficient risk management practices or the fallibility of their project management controls. Poorly structured incentives, obviously, can distort decisions as well as estimates. For example, any company that rewards good outcomes rather than good decisions motivates a project manager to escalate commitments to failing project, since the slim chance of turning the project around is better from the manager's perspective than the certainty of project failure. Base-Rate BiasBase-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
Trip insurance pricing illustrates 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. Lack of FeedbackForecasting errors are often attributed to the fact that most people don't get good feedback about the accuracy of their forecasts. We are all fairly good at estimating physical characteristics like volume, distance, and weight because we frequently make such estimates and get feedback about our accuracy. We are less experienced (and get less verification) when making forecasts for things that are more uncertain. Weather forecasters and bookmakers have opportunities and incentives to maintain records of their judgments and see when they have been inaccurate. Studies show that they do well in estimating the accuracy of their predictions. Advice for improving forecasts and estimates includes:
|
|||
Printable
version
Heuristics & Judgmental Biases