Garbage In, Garbage Out
Addressing biases is important even if you use formal tools and processes for evaluating and prioritizing projects. Such tools nearly always require inputs based on judgment. Poor judgments can result in GIGO (garbage-in/garbage out). Fortunately, research shows people are better at making the sort of limited, well-defined judgments that are typically required as inputs to well-designed decision models. Also, because a model breaks a problem down into individual pieces, different experts can be selected to focus on each.
There is some evidence that experts are better at avoiding biases when making judgments within their specific areas of expertise. Even so, it is clear that biases are common in many of the types of judgments that are required for prioritizing projects. For example, according to one study of IT projects, only 37% of projects were completed within original schedule estimates, and only 42% were on budget.
Studies show that knowing about biases can help people reduce them. A useful technique for reducing overconfidence bias prior to obtaining judgments from people is to demonstrate the 2/50 rule described above. Show people that overconfidence is something that affects them personally, not just others.
Training can also be effective. For example, studies show that formal training in statistics, in the classroom or in a 25-minute laboratory training program, improves judgments of probability. Many formal tools are available to avoid and correct for distortions in an individual's judgments, for example, techniques for "encoding" judgmental probabilities. There are also group facilitation techniques, such as Delphi and the Nominal Group Method, to minimize groupthink.
The best protection from bias comes from training, using formal techniques for obtaining important judgments, utilizing well-founded decision aids, and instituting rigorous decision processes that document the judgments and assumptions upon which choices are based. As stated by Ken Keyes, "To be successful in the business world we need to check our bright ideas against the territory. Our enthusiasm must be restrained long enough for us to analyze our ideas critically".[2]
Next month
Part 2 of this paper will describe the second reason organizations choose the wrong projects-failure to see the forest for the trees.
2. K. Keyes, Jr., Taming your Mind, Love Line Books, 1991.
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Estimating and Forecasting Biases
