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Is human probability judgment really so poor? 267
meterological conditions. Furthermore, they have considerable practice
in quantifying their internal state of uncertainty. These circumstances
may well be ideal for the relatively successful application of judgmental,
as compared with purely statistical, forecasting.
Additionally, accurate probability judgments have been demonstrated
in several real-world situations apart from weather forecasting. These
include horse racing, 26 prediction of future interest rates by bankers, 27
and prediction of the success of R&D projects. 28
Human judgment is particularly widely used in sales and economic
forecasting 29,30 although forecasts tend to be single point estimates
(e.g. ‘September’s sales will be 450 units’), rather than probabilities.
Judgment is used here either on its own or in combination with statistical
forecasts and there is much evidence to suggest that it can be useful
and accurate. 31 In reviews of research in this area, Bunn and Wright 32
and Webby and O’Connor 33 suggest that judgmental forecasting will
generally outperform statistical methods when contextual information
is available. Webby and O’Connor define contextual information as
‘information other than the time series and general experience, which
helps in the explanation, interpretation and anticipation of time series
behaviour’. Contextual information therefore embraces unusual events
that are too rare to include in a statistical model, but which have a
profound effect on the variable to be forecast (e.g. a strike at a rival
company which will lead to a massive, but temporary, increase in our
sales) and soft information (e.g. strong rumors that a rival is about to
launch an expensive advertising campaign).
A number of real-world studies support the conclusion of these
reviews. These include studies of forecasts made by managers of company
earnings, 34 workloads at a public warehouse 35 and the sales of
a leading manufacturer and marketer of consumer products. 36 All of
this, of course, does not necessarily suggest that these judgments did
not suffer from the use of heuristics, and the consequent biases, that
we discussed earlier, nor does it say that the judgments could not be
improved. 37,38,39 It does, however, show that human judgment can have
a valuable role to play in many real decision-making contexts, and that
its accuracy can outperform that of statistical methods.
7. People think in terms of frequencies not probabilities
Recently, Gerd Gigerenzer has argued that, from a strong frequency
view of probability, observed bias in probabilistic reasoning is not an
error since probability theory simply does not apply to single events. For