Forecast Friday Topic: Judgmental Bias in Forecasting

(Fortieth in a series)

Over the last several weeks, we have discussed many of the qualitative forecasting methods, approaches that rely heavily on judgment and less on analytical tools. Because judgmental forecasting techniques rely upon a person’s thought processes and experiences, they can be highly subjected to bias. Today, we will complete our coverage of judgmental forecasting methods with a discussion of some of the common biases they inspire.

Inconsistency and Conservatism

Two very opposite biases in judgmental forecasting are inconsistency and conservatism. Inconsistency occurs when decision-makers apply different decision criteria in similar situations. Sometimes memories fade; other times, a manager or decision-maker may overestimate the impact of some new or extraneous event that is occurring in the subsequent situation that makes it different from the previous; he/she could be influenced by his/her mood that day; or he/she just wants to try something new out of boredom. Inconsistency can have serious negative repercussions.

One way to overcome inconsistency is to have a set of formal decision rules, or “expert systems,” that set objective criteria for decision-making, which must be applied to each similar forecasting situation. These criteria would be the factors to measure, the weight each one gets, and the objective of the forecasting project. When formal decision rules are imposed and applied consistently, forecasts tend to improve. However, it is important to monitor your environment as your expert systems are applied, so that they can be changed as your market evolves. Otherwise, failing to change a process in light of strong new information or evidence is a new bias, conservatism.

Now, have I just contradicted myself? No. Learning must always be applied in any expert system. We live in a dynamic world, not a static one. However, most change to our environment, and hence our expert systems, doesn’t occur dramatically or immediately. Often, they occur gradually and more subtly. It’s important to apply your expert systems and practice them for time, monitoring anything else in the environment, as well as the quality of forecasts your expert systems are measuring. If the gap between your forecast and actual performance is growing consistently, then it might be time to revisit your criteria. Perhaps you assigned too much or too little weight to one or more factors; perhaps new technologies are being introduced in your industry.

Decision-makers walk a fine line between inconsistency and conservatism in judgmental forecasts. Trying to reduce one bias may inspire another.


Often, when there are shocks in the economy, or disasters, these recent events tend to dominate our thoughts about the future. We tend to believe these conditions are permanent, so we downplay or ignore relevant events from the past. So, to avoid recency bias, we must remember that business cycles exist, and that ups and downs don’t last forever. Moreover, we should still keep expert systems in place that force us to consider all factors relevant in forecasting the event of interest.


I’m guilty of this bias! Actually, many people are. Our projections are often clouded by the future outcomes we desire. Sometimes, we feel compelled to provide rosy projections because of pressure by higher-up executives. Unfortunately, optimism in forecasting can be very dangerous, and its repercussions severe when it is discovered how different our forecasted vs. actual results are. Many a company’s stock price has plunged because of overly optimistic forecasts. The best ways to avoid optimism are to have a disinterested third party generate the forecasts; or have other individuals make their own independent forecasts.


These are just a sample of the biases common in judgmental forecasting methods. And as you’ve probably guessed, deciding which biases you’re able to live with and which you are not able to live with is also a subjective decision! In general, for your judgmental forecasts to be accurate, you must consistently guard against biases and have set procedures in place for decision-making, that include learning as you go along.


Next Forecast Friday Topic: Combining Forecasts

For the last 10 months, I have introduced you to the various ways by which forecasts are generated and the strengths and limitations of each approach. Organizations frequently generate multiple forecasts based on different approaches, decision criteria, and different assumptions. Finding a way to combine these forecasts into a representative composite forecast for the organization, as well as evaluating each forecast is crucial to the learning process and, ultimately, the success of the organization. So, beginning with next week’s Forecast Friday post, we begin our final Forecast Friday mini-series on combining and evaluating forecasts.

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