JBR-08039; No of Pages 5
Journal of Business Research xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Journal of Business Research

Measuring overconfidence: Methodological problems and statistical artifacts
Henrik Olsson
Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany

a r t i c l e

i n f o

Article history:
Received 1 April 2013
Received in revised form 1 November 2013
Accepted 1 January 2014
Available online xxxx
Keywords:
Overconfidence
Underconfidence
Format dependence
Regression

a b s t r a c t
Psychological studies are frequently cited in the business and finance literature to bolster claims that various kinds of
economic disasters, from the large proportion of start-ups that quickly go out of business to the exaggerated confidence of financial investors, can be attributed to overconfidence. This article reviews some of the problems associated with concluding that people overestimate the accuracy of their judgments based on observed overconfidence
measured as the difference between mean subjective probability and proportion correct. Methodological and statistical artifacts, such as regression, can explain many of the observed instances of apparent overconfidence.
© 2014 Published by Elsevier Inc.

How confident are you that the following statement is true: “One of
the most significant and irrefutable findings of behavioral psychologists
is that people are overconfident in their judgments and over-estimate
the reliability of their judgments” (Parikh, 2009, p.142). This bold statement was given by Parag Parikh in his recent book Value Investing and
Behavioral Finance. And he is not alone. A quick look in books or articles
that mention overconfidence in cognitive psychology, judgment and
decision making, behavioral economics, behavioral finance and so on,
unveils similar statements (see Table 1 for examples). If you have read
these books or articles, you would probably be very confident that
Parikh's statement is true.
Psycho; logical studies on overconfidence are frequently cited in the
business and finance literature to bolster claims that various kinds of
economic disasters, from the large proportion of start-ups that quickly
go out of business to the exaggerated confidence of financial investors,
can be attributed to overconfidence. As Griffin and Tversky (1992) emphasized, “The significance of overconfidence to the conduct of human
affairs can hardly be overstated” (p. 432). In this article I will give a
brief exposition of realism of confidence research that challenges the
prevailing view.

literature, overestimation, overplacement, and calibration of subjective
probabilities (or realism of confidence). Overestimation is measured
by comparing a person's performance with that person's belief of own
performance. For example, the number of correct answers a person
achieves on a test is compared to that person's estimate of how many
questions she thinks she answered correctly. Overplacement is measured by comparing a person's performance with others' performances.
For example, a person's actual location in a test score distribution (percentile) is compared with that person's estimate where she is located in
the distribution. The calibration of subjective probabilities is measured
by comparing subjective probability judgments with the corresponding
objective probabilities. For example, a person's mean subjective probability estimate of choosing the correct answer on a test is compared to
that person's relative frequency of correct answers.
It is currently unknown to what extent these three different forms of
overconfidence represents the same psychological construct, as only a
handful of studies have investigated two or more ways of measuring
overconfidence (but see Moore & Healy, 2008; Larrick, Burson, & Soll,
2007). In this article I will mainly focus on the last, overconfidence as
(mis-)calibration (for a discussion of variants of overconfidence and potential benefits of functional overconfidence, see Gigerenzer, Fiedler, &
Olsson, 2012; Moore & Healy, 2008; Mousavi & Gigerenzer, 2011).

The many faces of overconfidence

Calibration of subjective probabilities

The term overconfidence has been used to describe many different
phenomena from hubris to “unskilled and unaware of it” effects
(Kruger & Dunning, 1999), measured in various ways. At least three different definitions of overconfidence are used in the psychological

Studies in the realism of confidence, or calibration, tradition usually
employ one of three response formats, the half-range format, the fullrange format, or the interval estimation format. In a half-range task,
the participants select one of two presented answers and assess the
probability that the selected answer is correct on a scale from .5 to 1
(usually expressed as percentages).

Introduction

E-mail address: h.olsson@warwick.ac.uk.

http://dx.doi.org/10.1016/j.jbusres.2014.03.002
0148-2963/© 2014 Published by Elsevier Inc.

Please cite this article as: Olsson, H., Measuring overconfidence: Methodological problems and statistical artifacts, Journal of Business Research
(2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.002

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H. Olsson / Journal of Business Research xxx (2014) xxx–xxx

Table 1
Statements on the pervasiveness of overconfidence.
Source

Quote

Blake (2009). The art of decisions: how to manage in an uncertain world. Harlow, “Studies have been made of physicians, clinical psychologists, lawyers, negotiators, engineers, and
England: FT Prentice Hall.
security analysts. The evidence is overwhelming. They all show the same systematic
overconfidence in the accuracy of their judgments.” (p. 159)
Charupat, Deaves, and Lüders (2005). Knowledge vs. knowledge perception:
“You, like most people (including the authors of this article), are overconfident. Your perception of
implications for financial professionals. Journal of Personal Finance, 4, 50–61.
your knowledge exceeds your actual knowledge.” (p. 52)
Meloy, Russo, and Miller (2006). Monetary incentives and mood. Journal of
“The phenomenon of overconfidence is one of the most robust findings in the decision and
Marketing Research, 3, 267–275.
judgment literature.” (p. 272)
Montier (2007). Behavioural investing: a practitioner's guide to applying
“The two most common biases that psychologists have documented are overoptimism and
behavioural finance. Chichester, England: Wiley.
overconfidence […]. [I]f we ask people for a forecast and then ask them for the 98% confidence
intervals, so that the true answer should lie outside of the bounds just 2% of the time, it tends to lie
outside the bounds 30–40% of the time! People are simply far too sure about their ability to
predict.” (p. 109)
Odean (1998). Volume, Volatility, Price, and Profit When all traders are above
“People are overconfident. Overconfidence affects financial markets.” (p. 1887)
average. Journal of Finance, 53, 1887–1934.
Redhead (2008). Personal finance and investments: A behavioral perspective.
“People tend to think they are better than they really are.” (p. 24)
New York: Routledge.
Schaefer, Williams, Goodie, and Campbel (2004). Overconfidence and the big five. “It has been consistently observed that people are generally overconfident when assessing their
Journal of Research in Personality, 38, 473–480.
performance.” (p. 473)
Sternberg (2008). Cognitive psychology (5th Ed.). Belmont, CA: Wadsworth.
“Another common error is overconfidence […]. In general, people tend to overestimate the
accuracy of their judgments.” (p. 495).

Which country has a population with a higher mean life expectancy?
a. Indonesia
50%
Random

60%

b. Sudan
80%

70%

90%

100%
Certain

The overconfidence score is calculated as the difference between the
mean subjective probability of a correct answer and the proportion of
correct answers. A positive value indicates overconfidence and a negative value indicates underconfidence. For example, if the participants
on average assess the probability that they have chosen the correct
answer to be .8 (or 80%), but only have 60% correct, overconfidence is .2.
In a full-range task the probability that a proposition is true is
assessed between 0 and 1 (again usually expressed as percentages).
The population of Indonesia has a higher mean life expectancy than
the population of Sudan. What is the probability that this statement is
true?
0%
10%
20%
Certainly false

30%

40%

50%

60%

70%

80%

90%
100%
Certainly true

The overconfidence score is computed by assuming that participants
would have decided that the proposition is true when they give probability ratings larger than .5, and that the statement is false when they
give ratings below .5. For probabilities below .5 the inferred half-range
probabilities are 1 minus the estimated probabilities. The probabilities
of .5 are randomly assigned as favoring either the truth or the falsity
of the proposition. The mean of the inferred probabilities is then compared to the inferred half-range proportion correct in the manner of
half-range data and the resulting score is interpreted in the same way
as for the half-range method.
In an interval estimation task the participant is confronted with a
statement such as:
Assess the (smallest) interval within which you are 80% certain (probability .8) that
the mean life expectancy of the population of Indonesia lies:
Between _______ years and_______ years

Here overconfidence scores are computed from the difference between the stated probability interval and the observed event proportion
within this interval.
The data from calibration studies are usually summarized in a calibration diagram. Fig. 1A shows a calibration diagram adapted from Juslin
(1994) with data from two conditions (the conditions are explained
below in the section on overconfidence as a consequence of nonrepresentative stimulus sampling). In the selected condition the calibration

curve shows the hallmarks of overconfidence as it is below the diagonal,
which represent perfect calibration, throughout most of the range
(the overconfidence score is .08). In the random condition the calibration
curve is closer to the diagonal with a tendency toward underconfidence
in the lower part (the overconfidence score is − 0.04). Note that
miscalibration in the sense of regression of the calibration curve around
the midpoint of the scale does not imply overall overconfidence for the
half-range format, the difference between mean subjective probability
and proportion correct can still be zero even when the calibration
curve has a slope less than one.
Fig. 1B shows a full-range calibration curve from Juslin, Winman, and
Olsson (2003). Here the curve is very close to the diagonal and there is
close to zero overconfidence (the overconfidence score is 0.02). In contrast to the half-range calibration curve, regression around the midpoint
in the full-range curve implies more overconfidence. These differences
between the two response formats are important to keep in mind for
the discussion below of regression as an explanation of observed overconfidence in full-range tasks and the finding that different response
formats elicit different amounts of overconfidence.
The most common finding with general knowledge items has been
the overconfidence phenomenon, where the mean subjective probability
exceeds the overall proportion correct (see Keren, 1991; Lichtenstein,
Fischhoff, & Phillips, 1982; McClelland & Bolger, 1994, and Yates, 1990,
for reviews). The early explanations of overconfidence revolved around
the idea of a general processing bias, either due to motivational factors
(Taylor & Brown, 1988) or cognitive factors (e.g., Koriat, Lichtenstein, &
Fischhoff, 1980).
In the early nineties a new and somewhat provocative explanation
of the overconfidence phenomenon appeared in terms of what has
been referred to as the ecological models (McClelland & Bolger, 1994).
The most well known and elaborate theory was the theory of probabilistic mental models (Gigerenzer, Hoffrage, & Kleinbölting, 1991; see also
Björkman, 1994; Juslin, 1993, 1994; Juslin, Olsson, & Björkman, 1997).
The many problems with overconfidence
Overconfidence as a consequence of nonrepresentative item sampling
The main idea incorporated in the ecological models was that in
many circumstances, participants may be reasonably calibrated to the
probability structure of their environment. This was premised on cognitive adjustment, that is, that participants have had sufficient experience
with their environment to acquire accurate cognitive representations of
the ecological probabilities (Gigerenzer et al., 1991). For a number of

Please cite this article as: Olsson, H., Measuring overconfidence: Methodological problems and statistical artifacts, Journal of Business Research
(2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.002

H. Olsson / Journal of Business Research xxx (2014) xxx–xxx

3

reasons (cf. Juslin, 1993, 1994), the traditional strategies of item selection have the side-effect of over-representing items where the probabilistic inferences lead to erroneous answers, at the expense of items
where the same inferences are successful.
Thus, if items are randomly selected from a reference class of objects
defined by a natural environment, overconfidence should diminish
or even disappear. A number of studies provided support for this hypothesis (e.g., Gigerenzer et al., 1991; Juslin, 1993, 1994; Winman,
1997). The difference between selected sampling (i.e., the condition
included items that naïve participants had chosen to be good
knowledge-discriminating items in a pilot study) and random sampling
is illustrated in Fig. 1A. Some later studies challenged this conclusion
(e.g., Griffin & Tversky, 1992), but the issue of the effects of different
sampling schemas on overconfidence in general knowledge tasks with
the half-range format appears to have been resolved with the metaanalytic review in Juslin et al. (2000). They analyzed 130 studies with
and without random sampling to see what the evidence really says.
They showed that overconfidence bias indeed disappeared across all
35 studies with random sampling, with the difference between mean
confidence and mean proportion correct being indistinguishable from
zero.
Overconfidence as a consequence of regression
A more recent proposal is that overconfidence may reflect a
regression-like side-effect of stochastic components of the judgment
process (Erev, Wallsten, & Budescu, 1994; Pfeifer, 1994; Soll, 1996;
Dawes & Mulford, 1996). The argument is simple. Due to the merely
correlative relation between subjective and objective probabilities
there will be regression when one of the variables is plotted against
the other. An imperfect correlation implies that when the reported confidence ratings are high, the corresponding proportions correct will be
smaller, looking like overconfidence. For instance, when one looks at
all cases where people said that they were “90% confident that the statement is true,” the mean proportion of correct answers will be lower,
such as 80%, depending on the exact correlation between confidence
and proportion correct. If one estimates the confidence judgments
from proportion correct (rather than vice versa), then one should
get the mirror result: a pattern that looks as if there was underconfidence bias. So, for instance, when one looks at all items that the
participants got 100% correct, one will find that the average confidence
was lower, such as 80%. This appears to be underconfidence. In contrast,
when one looks at all items for which participants were 100% confident,
one finds that the average proportion correct was lower, such as 80%.
This appears to be overconfidence. Erev et al. (1994) showed for
three empirical data sets that regression toward the mean accounted
for practically all the effects that would otherwise have been attributed
to overconfidence or underconfidence, depending on how one plotted
the data.
The hard–easy effect as a consequence of regression
Regression and regression like effects can also explain another
pervasive finding in calibration research that people seem to be
underconfident in easy tasks and overconfident in hard tasks. In the absence of any bias, regression toward the mean implies that the largest
positive difference will be found for easy items, that is, when proportion
correct is high. Regression also implies that this difference will become
smaller, and eventually turn into a negative difference, when items become more and more difficult. In other words, regression toward the
mean alone produces the pattern that has been interpreted as a cognitive hard–easy effect. There are also several other problems associated
with the interpreting the hard–easy effect (Juslin et al., 2000). One is
the linear dependency between overconfidence and proportion correct.
As proportion correct is part of the overconfidence score, measurement
error in proportion correct alone can produce the hard–easy effect.

Fig. 1. Panel A shows the half-range empirical calibration curves for two conditions in
Juslin (1994). The dotted line represents perfect calibration. Adapted from “The overconfidence phenomenon as a consequence of informal experimenter-guided selection of
almanac items”, by Juslin (1994) Organizational Behavior and Human Decision Processes,
57, p. 238. Copyright 1994 by Academic Press Inc. Panel B shows a full-range empirical calibration curve from Juslin et al. (2003). The dotted line represents perfect calibration.
Adopted from “Calibration, additivity, and source independence of probability judgments
in general knowledge and sensory discrimination tasks”, by Juslin et al. (2003) Organizational Behavior and Human Decision Processes, 92, p. 44. Copyright 2003 by Elsevier
Science.

Another problem is scale-end effects. For example, in a half range task
when proportion correct is .5 or less overconfidence can only be 0 or
positive and when proportion correct is 1 overconfidence can only be
less than zero or negative and response error in the use of the probability scale will appear as a hard–easy effect as the error has only one way
to go.
The hard–easy effect tells us very little about how good or bad
people's judgments are. Merkle (2009) derived mathematically necessary and sufficient conditions for observing a hard–easy effect under realistic assumptions. He concluded that “both perfectly calibrated judges
(at the item level) and terrible judges (whose confidence is unrelated to
the stimulus) exhibit the hard–easy effect” (p. 211).
Overconfidence as a result of task format
We already saw that overconfidence decreases or disappears when
items are randomly sampled from an environment and turns into
underconfidence if objective probability is treated as the independent
variable. To what extent a study finds overconfidence is also highly dependent on the response format used. Format dependence refers to the
finding that you can simultaneously observe underconfidence and

Please cite this article as: Olsson, H., Measuring overconfidence: Methodological problems and statistical artifacts, Journal of Business Research
(2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.002

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H. Olsson / Journal of Business Research xxx (2014) xxx–xxx

overconfidence for the same tasks depending on the choice of response
format (Juslin, Wennerholm, & Olsson, 1999; Juslin et al., 1997).
For example, the two experiments in Juslin et al. (1999) found
underconfidence for the half-range format, close to zero or moderate
overconfidence for the full range format and massive overconfidence
for the interval estimation format.
The difference between the half-range format and the full-range format is a straight forward consequence of how response error interacts
with the response format to produce overconfidence. Simulations in
Juslin et al. (1997) show that even perfectly calibrated responses that
have been perturbed by response error (e.g., emanating from inconsistencies in how the probability scale is used) exhibit format dependence
effects, with more overconfidence for the full-range format than for the
half range format. The explanation for this is that the regression, or
rotation, of the calibration curve in the full range format is centered approximately around the mean of the full range scale (0.5), whereas with
the half-range format the rotation is centered around the mean of the
half range scale (0.75). This means that with the full-range format the
response error effects are perfectly confounded with the standard measure of overconfidence, that is, more response error pulls toward more
overconfidence. With the half-range format the effect might either be
over- or underconfidence depending on the task difficulty.
The response error account also predicts more overconfidence for
the interval estimation format than for the other two formats, but not
the extreme levels of overconfidence that is usually observed in interval
estimation tasks. Recently, however, a new model based on the idea that
people na vely use sample estimates of variability to form intervals, predicts the observed levels of overconfidence (Juslin, Winman, & Hansson,
2007). Also, it has been shown that the massive overconfidence in the
interval estimation format can be turned into close to zero overconfidence if participants instead evaluate given intervals (e.g., “The population of Thailand lies between X and Y million. Assess the probability that
the statement is correct”; Winman, Hansson, & Juslin, 2004).
Underconfidence in sensory discrimination
Another finding that speaks against the general assertion that people
are overconfident is the pervasive underconfidence found in sensory
discrimination tasks. The sensory domain was only sporadically explored
before the nineties (e.g., Keren, 1988; although the results in the classic
study by the father of American pragmatism, Peirce & Jastrow, 1884
can be viewed as the first demonstration of underconfidence in sensory
discrimination) and the tasks were not strictly sensory in nature (e.g.,
Dawes, 1980). The first study that truly explored the sensory domain
with a weight discrimination task observed pervasive underconfidence
(Björkman, Juslin, & Winman, 1993). This study and others (e.g.,
Winman & Juslin, 1993) led to a debate about possible differences in realism of confidence in the cognitive and the sensory domains (e.g., Baranski
& Petrusic, 1994; Olsson & Winman, 1996), where some researchers
claimed that there was no difference in over/underconfidence between
the two domains (e.g., Baranski & Petrusic, 1994).
By now it seems safe to conclude that there is a difference between
the two domains in terms of calibration, with more underconfidence
in the sensory domain. For example, Juslin, Olsson, and Winman
(1998), reviewed aggregated data from 21 sensory discrimination
tasks and 44 general knowledge inferential tasks with items randomly
sampled from a reference class of objects defined by a natural environment, a clear main effect of sensory versus cognitive tasks revealed
more underconfidence for sensory discrimination (.01 vs. − .10; see
also Juslin & Olsson, 1997, in which the authors present a computational
model that predicts underconfidence in sensory discrimination).
Overconfidence in business and finance
So far, I have reviewed studies from psychology and judgment and
decision making highlighting some of the problems associated with

concluding that people overestimate the accuracy of their judgments
based on observed overconfidence scores. In business and finance,
however, these studies have had little impact. They continue to cite
that researchers take the (apparent) overconfidence observed in psychological experiments as evidence that most people are overconfident
most of the time and use it as an assumption in their models. For example, Odean (1998) uses the assertion that people are overconfident
(conceptualized as overestimation of the precision of private information) as an assumption in his models of traders in financial markets
and concludes that, among other things, that overconfidence increases
expected trading volume and decreases the expected utility of overconfident traders. Overconfidence in his models, however, is only an untested assumption and in many of the studies that test Odean's
models “overconfidence is neither directly observed nor manipulated”
(Barber & Odean, 2001, p. 264). Direct tests of the link between overconfidence and trading volume are rare. Recent evidence, however, suggests that some measures of overconfidence are unrelated to trading
volume. Glaser and Weber (2007) investigated the relation between
trading volume and two measures of overconfidence, one derived
from an interval estimation task and another derived from a better
than average task. Using data from approximately 3000 online brokers
they found that overconfidence in the interval estimation task was
unrelated to trading volume, while an index of the better than average
effect traded more. This result is consistent with results from experimental markets, where overconfidence measured by an interval estimation task is unrelated to trading volume (Biais, Hilton, Mazurier, &
Pouget, 2005).
Does overconfidence exist?
A reader might ask, “So, does overconfidence exist?” The message of
this article is not that all instances of observed overconfidence are more
apparent than real. Rather, the message is that researchers need to be
careful in making assumptions about overconfidence and its effects on
behavior without considering the conceptual, statistical, and methodological problems associated with concepts of overconfidence. The answer, then, must be: “It depends on what you mean by overconfidence
and how you measured it”. This might seem unsatisfactory, but the concept of overconfidence has so many different meanings and can be measured in so many ways, that a more specific answer cannot be given
without knowing which form of overconfidence is studied and what
methods were used to elicit answers from people.
In the case of overconfidence as miscalibration, the topic of most of
this article, some concrete advice can be given. 1) Choose your sample
of items carefully, preferably by randomly sample a large pool of
items. 2) If a researcher is interested in overconfidence for different
levels of difficulty, some of the linear dependency between overconfidence and proportion correct should be removed by estimating proportion correct and overconfidence on different item sets (i.e., the same
estimate of proportion correct never enters twice, both as an independent variable and as part of the dependent variable overconfidence,
see Klayman, Soll, González-Vallejo, & Barlas, 1999). 3) Beware of
scale end and regression effects. Random response error pulls higher
probability estimates downwards and lower estimates upwards. This is
difficult to correct for, but models that include an error parameter can
be used (Juslin et al., 2000). 4) The traditional interval estimation format
is probably not advisable to use. Instead, has participants evaluate given
intervals (e.g., “The population of Indonesia lies between X and Y million.
Assess the probability that the statement above is correct”).
Conclusion
This article reviews some of the problems associated with concluding that people overestimate the accuracy of their judgments based on
observed overconfidence scores. Methodological and statistical artifacts
can explain many of the observed instances of apparent overconfidence.

Please cite this article as: Olsson, H., Measuring overconfidence: Methodological problems and statistical artifacts, Journal of Business Research
(2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.002

H. Olsson / Journal of Business Research xxx (2014) xxx–xxx

In the business and finance literature, however, overconfidence is for
the most part taken as a given. Little consideration is given to the
many ways overconfidence can be conceptualized and the methodological and statistical artifacts that come along with attempts of measuring
some forms of overconfidence. There is clearly a need for more research
investigating the assumptions in finance and business models as well as
the proxies for overconfidence used in the empirical literature.
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Please cite this article as: Olsson, H., Measuring overconfidence: Methodological problems and statistical artifacts, Journal of Business Research
(2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.002