Bias Traps:
How and Why
Are Spun
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in quality and validity of data that may be pre-
sented in support of a forecast. In this chapter we
consider how data—whether good or bad in itself—
can be interpreted or misinterpreted in creating a
As established earlier, forecasting is done for
the advancement of an individual, organization,
group, nation, or humanity as a whole. This bene-
fit can come in the form of anticipating change and
aligning with it, influencing future outcomes to
one’s own advantage, or both. As we move to con-
sider the interpretation of facts and data, it is these
“political” aspects of forecasting that become cen-
tral. The forecast filterer needs to take a clear-eyed
view, recognizing that people rarely tell us anything
out of the goodness of their hearts, purely to inform or educate
us. And, just as there is no “value-free” look at history, so too there
is no value-free look to the future. We need to ask who is talking
to us about the future, why he or she is doing it, and what his or
her agenda is, and be ready to mentally rebalance the forecast in-
terpretations that are presented.
Natural Versus Intentional Bias
The key issue in interpreting data is bias—the ways in which the
interpreter will, consciously or unconsciously, bring a personal or
organizational preference to bear in selecting data or extracting mean-
ing from it. But not all bias is manipulative and cynical. Even where
a forecaster is using factually valid and
technically accurate information, a point
of view will always insert itself into the
analysis. Two people with the identical in-
formation will not necessarily interpret it
the same way or derive the same forecast
from it. Pure objectivity is not possible,
and interpretations of future possibilities from current trends and un-
certainties can likewise never be objective.
This saves us from chasing a chimera. Natural bias is not neces-
sarily bad, or an “error” to be corrected. It is inevitable. Everyone
has a point of view. Also, forecasters are always shaping informa-
tion, tidying it up, and glossing over weaknesses, often without re-
alizing it. Accurate information is no guard against this. And
everyone’s observations and opinions are deeply influenced by his
or her own culture, education, personal experiences, and contex-
tual incentives. These interpretations are an inescapable product of
the way forecasters see and organize the world, and the beliefs
and preferences they bring to bear. The role of these perceptual
Pure objectivity is not pos-
sible, and interpretations
of future possibilities from
current trends and uncer-
tainties can likewise never
be objective.
frames and paradigms in creating natural bias are dealt with in
Chapter 4.
However, at a certain point natural bias merges into an inten-
tional, calculated act. A line is crossed where interpretation be-
comes something altogether more premeditated and manipulative—
the intention to misrepresent the evidence on purpose, or to skew
its interpretation, to lead us to a forecast conclusion that creates ad-
vantage for the forecaster or forecast institution. In future-aligning
forecasts—where the forecaster has no particular interest in one
set of outcomes over another—the bias is likely to be natural bias.
By contrast, in the future-influencing forecast, the likelihood of
conscious bias is far greater. A forecaster who sees personal or
institutional benefit in a preferred future coming about, or a dysto-
pia avoided, has clear motive to promote the necessary future-
influencing perspective, while submerging alternative inferences
and opinions on an “ends-justify-the-means” basis.
The following are the particular markers of intentional bias in
Selective choice of data or omission of conflicting data.
Here data is quoted accurately but countering evidence or op-
posing numbers are not given. This is the oldest trick in the
book, but that doesn’t mean it is not practiced every day. It is
often difficult to spot if you are not expert in the field. The
only reliable way around this is to seek out forecasts on sim-
ilar topics from people or organizations with different interests.
Prejudicial organization and emphasis. Another standard
technique is devoting uneven time or space to contrary evi-
dence. By running one set of data more prominently than an-
other, or by prejudicial organization, the forecaster is able to
highlight supporting evidence or downplay countering claims.

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