top answer: PSYCHOLOGY Research: Learning a Little About Something Makes Us Overcondent by Carmen Sanchez a



Research: Learning a Little About
Something Makes Us
by Carmen Sanchez and David Dunning

Don't use plagiarized sources. Get Your Custom Essay on
top answer: PSYCHOLOGY Research: Learning a Little About Something Makes Us Overcondent by Carmen Sanchez a
Just from $10/Page
Order Essay

MARCH 29, 2018


As former baseball pitcher Vernon Law once put it, experience is a hard teacher because it gives the

test first, and only then provides the lesson.

Perhaps this observation can explain the results of a survey sponsored by the Association of

American Colleges & Universities. Among college students, 64% said they were well prepared to

work in a team, 66% thought they had adequate critical thinking skills, and 65% said they were

proficient in written communication. However, among employers who had recently hired college

students, less than 40% agreed with any of those statements. The students thought they were much

further along in the learning curve toward workplace success than their future employers did.

Overcondence Among Beginners

Our research focuses on overconfidence as people tackle new challenges and learn. To be a beginner

is to be susceptible to undue optimism and confidence. Our work is devoted to exploring the exact

shape and timeline of that overconfidence.

One common theory is that beginners start off overconfident. They start a new task or job as

“unconscious incompetents,” not knowing what they don’t know. Their inevitable early mistakes

and miscues prompt them to become conscious of their shortcomings.

Our work, however, suggests the opposite. Absolute beginners can be perfectly conscious and

cautious about what they don’t know; the unconscious incompetence is instead something they

grow into. A little experience replaces their caution with a false sense of competence.

Specifically, our research focused on the common task of probabilistic learning in which people

learn to read cues from the environment to predict some outcome. For example, people must rely on

multiple signals from the environment to predict which company’s stock will rise, which applicant

will do the best job, or which illness a patient is suffering from. These can be hard tasks — and even

the most expert of experts will at times make the wrong prediction — but a decision is often essential

in many settings.

In a laboratory study, we asked participants to imagine they were medical residents in a post-

apocalyptic world that has been overrun by zombies. (We were confident that this would be a new

scenario to all our participants, allowing them all to start as total novices.) Their job, over 60

repeated trials, was to review the symptoms of a patient, such as whether the patient had glossy

eyes, an abscess, or brain inflammation, and diagnose whether the patient was healthy or infected

with one of two zombie diseases. Participants needed to learn, by trial and error, which symptoms to

rely on to identify zombie infections. Much as in a real-world medical diagnosis of a (non-zombie)

condition, the symptoms were informative but fallible clues. There were certain symptoms that

made one diagnosis more likely, but those symptoms were not always present. Other potential

symptoms were simple red herrings. Participants diagnosed patients one at a time, receiving

feedback after every diagnosis.

The Beginner’s Bubble

We found that people slowly and gradually learned how to perform this task, though they found it

quite challenging. Their performance incrementally improved with each patient.

Confidence, however, took quite a different journey. In each study, participants started out well-

calibrated about how accurate their diagnoses would prove to be. They began thinking they were

right 50% of the time, when their actual accuracy rate was 55%. However, after just a few patients,

their confidence began skyrocketing, far ahead of any accuracy they achieved. Soon, participants

estimated their accuracy rate was 73% when it had not hit even 60%.


It appears that Alexander Pope was right when he

said that a little learning is a dangerous thing. In our

studies, just a little learning was enough to make

participants feel they had learned the task. After a

few tries, they were as confident in their judgments

as they were ever going to be throughout the entire

experiment. They had, as we termed it, entered into

a “beginner’s bubble” of overconfidence.

What produced this quick inflation of confidence? In

a follow-up study, we found that it arose because

participants far too exuberantly formed quick, self-

assured ideas about how to approach the medical

diagnosis task based on only the slimmest amount of

data. Small bits of data, however, are often filled

with noise and misleading signs. It usually takes a

large amount of data to strip away the chaos of the

world, to finally see the worthwhile signal. However,

classic research has shown that people do not have a

feel for this fact. They assume that every small

sequence of data represents the world just as well as long sequences do.

But our studies suggested that people do eventually learn — somewhat. After participants formed

their bubble, their overconfidence often leveled off and slightly declined. People soon learned that

they had to correct their initial, frequently misguided theories, and they did. But after a correction

phase, confidence began to rise again, with accuracy never rising enough to meet it. It is important

to note that although we did not predict the second peak in confidence, it consistently appeared

throughout all of our studies.


A Real-World Bubble

The real world follows this pattern. Other research

has found that doctors learning to do spinal surgery

usually do not begin to make mistakes until their

15th iteration of the surgery. Similarly, beginning

pilots produce few accidents — but then their

accident rate begins to rise until it peaks at about

800 flight hours, where it begins to drop again.

We also found signs of the beginner’s bubble outside

of the laboratory. As with probabilistic learning, it

has been shown that most people under the age of

18 have little knowledge of personal finance. Most

primary and secondary educational systems do not

teach financial literacy. As such, personal finance is

something most learn by trial and error.

We found echoes of our laboratory results across the

life span in surveys on financial capability

conducted by the Financial Industry Regulatory

Authority. Each survey comprised a nationally

representative sample of 25,000 respondents who

took a brief financial literacy test and reported how knowledgeable about personal finance they

believed they were. Much like in the laboratory, both surveys showed that real financial literacy

arose slowly, incrementally, and uniformly across age groups.

Self-confidence, however, surged between late adolescence and young adulthood, then leveled off

among older respondents until late adulthood, where it began to rise again — a result perfectly

consistent with our laboratory pattern.

It is important to note that our work has several limitations. In our experiments, participants

received perfect feedback after each trial. In life, consistent feedback like this is often unavailable.

Also, our tasks traced how confidence changed as people learned truly novel tasks. There are plenty

of tasks people learn in which they can apply previous knowledge to the new task. We do not know

how confidence would change in these situations. Relatedly, we cannot be certain what would

happen to overconfidence after the 60th trial.

With that said, our studies suggest that the work of a beginner might be doubly hard. Of course, the

beginner must struggle to learn — but the beginner must also guard against an illusion they have

learned too quickly. Perhaps Alexander Pope suggested the best remedy for this beginner’s bubble

when he said that if a few shallow draughts of experience intoxicate the brain, the only cure was to

continue drinking until we are sober again.

Carmen Sanchez is a PhD candidate in Social and Personality Psychology at Cornell University. She studies how
perceptions of abilities change as people learn, cultural differences in self-enhancement, and nancial decision-making.

David Dunning is a Professor of Psychology at the University of Michigan. His research focuses on the psychology of
human misbelief, particularly false beliefs people hold about themselves.

Sloan Management Review
Reprint Series




Winter 1992


Number 2

– — — ————-‘——� ——-

Managing Overconfidence

1. Edward Russo • Paul 1. H. Schoemaker


an understanding of the limits of our knowledge. Unfortunately, we tend

to have a deeply rooted overconfidence in our beliefs and judgments.

Because metaknowledge is not recognized or rewarded in practice, nor in­

stilled during formal education, overconfidence has remained a hidden

flaw in managerial decision making. This paper examines the costs, causes,

and remedies for overconfidence. It also acknowledges that, although

overconfidence distorts decision making, it can serve a purpose during de­
cision implementation. 00

]. Edward Russo is Proftssor of Mark�ting

a n d B�havioral Scimu at th� joh n s o n

Graduau School o f Manag�mmt, Corn�//

Univmity. Paul]. H. Scho�k” is Associ­

au Proftssor of Straugy at th� Graduau

School of Businm, th� Univn1ity of Chicago.

They r�cmtly publish�d a book on managm­

al decision making. Decision Traps (Simon

and Schust”, 1990.)

To know that we know what we know and that we
do not know what we do not know, that is true
knowledge. -Confucius

hilosophers and writers have long tried to raise
awareness about the difficulty of balancing confi­
dence with realism, y et the consequences of un­

supportable confidence continue to plague businesses.
Managers deal in opinions – they are bombarded with
proposals, estimates, and predictions from people who
sincerely believe them. But experience tells managers to
suspect the certainty with which these beliefs are stated.
For instance:
• A leading U.S. manufacturer, planning production ca­
pacity for a new factory, solicited a projected range of
sales from its marketing staff. The range turned out to be
much too narrow and, consequently, the factory could
not adjust to unexpected demand.
• A loan officer at a major commercial bank felt that his
colleagues did not understand their changing competi­
tion as well as they thought they did and were refusing
to notice signs of coming trouble.
• In the early 1970s, Royal Dutch/Shell grew concerned
that its y oung geologists too confidently predicted the
presence of oil or gas, costing the company millions of
dry-well dollars.


• The sales head for Index Technology, a new software
venture, repeatedly received unrealistic sales predictions,
not only on amounts but also on how soon contracts
would be signed.

Managers know that some opinions they receive
from colleagues and subordinates will be accurate and
others inaccurate, even when they are all sincerely held
and persuasively argued. Moreover, given any strongly
held opinion, one seldom has to look far to find an op­
posing view that is held no less firmly. We do not even
have to favor a position now to reserve the right to hold
a foture position. One of us attended a faculty meeting
at which a senior faculty member had been notably
silent during a heated debate. W hen asked for his posi­
tion, he replied, “I feel strongly about this; I just haven’t
made my mind up which way.”

People are often unjustifiably certain of their beliefs. As
a case in point, the manufacturer cited above accepted the
staff’s confidently bracketed sales projections of twenty­
three to thirty-five units per day and designed its highly
automated factory to take advantage of that narrow range.
Then, because of a worldwide recession, sales dropped
well below twenty-three units per day. The plant was
forced to operate far below its breakeven point and piled
up enormous losses. Instead of being the best of the com­
pany’s production facilities, it became the biggest loser.


Confidence Quiz

For each ot the following questions. provide a low and a high estimate such that you are 90

ranges for questions like: “What was
the total dollar value of new com­
mercial loans made by [Competitor
X] last year?” and “What is the total
number of commercial loan officers
at the sixth- through tenth-ranking
banks in [our city]?”

1 percent certain the correct answer will fall within these limits. You should aim to have 90 ,
percent hits and 10 percent misses. The correct answers are provided at the end of this arti­
cle so that you can compute how close you come to the ideal level of one miss in ten.

90% Confidence Range ,

1. How many patents did the U.S. Patent and Trademark
Office issue in 1990?

2. How many of Fortune’s 1990 “Giobal500,” the world’s
biggest industrial corporations (in sales). were Japanese?

3. How many passenger arrivals and departures were there
at Chicago’s O’Hare airport in 1989?

4. What was the total audited worldwide daily circulation of
the Wall Street Journal during the first half of 1990?

5. How many master’s degrees in business or management
were conferred in the United States in 1987?

6. How many passenger deaths occurred worldwide in sche­
duled commercial airliner accidents in the 1980s?

7. What is the shortest navigable distance (in statute miles)
between New York City and Istanbul?

8. What was General Motors’ total worldwide factory sales
of cars and trucks (in units) in the 1980s?

9. How many German automobiles were sold in Japan in 19897
1 10. What was the total U.S. merchandise trade deficit with
i Japan (in billions) in the 1980s?

lower upper
We believe that, like the loan offi­

cer’s reluctant boss, you will be sur­
prised at how poorly you do on the
test below. For each of the ten quanti­
ties in the quiz, simply give a low
guess and a high guess such that you
are 90 percent sure the true value will
lie between them. Try it before read­
ing further, particularly if you are apt
to be confident of your ability to pre­
dict accurately.


l�——–�——�—-�——–�——– — – – – – ——–
The confidence quiz measures some­
thing called metaknowledge: an appre­
ciation of what we do know and what

A Test of Confidence

How should managers deal with the often unreliable
opinions they receive? The answer lies in recognizing
that most people’s beliefs are distorted by deep-seated
overconfidence. Once we understand its nature and
causes, we can better devise plans for controlling it. The
first step is to document and measure the problem’s

Recall the loan officer who believed that his col­
leagues were overconfident about their competitors. He
went to his boss with this concern and proposed mea­
suring the degree of confidence his colleagues had in
their knowledge about the bank’s competitors. The boss
insisted there was nothing to worry about: “No one is
more realistic than a banker.” Despite this overconfident
answer, the boss agreed to the test – but only he would
take it. To his surprise, he failed miserably; to his credit,
he then asked all eleven other loan officers to take the
same test. Every one of them flunked.

The loan officer’s test asked for both best estimates
and ranges of confidence around those estimates. The
“Confidence Quiz” shown here is just such a test, one
that involves general business rather than company­
specific questions. The bank test included confidence


we do not know. Normally, we define knowledge as
consisting of all the facts, concepts, relationships, theo­
ries, and so on that we have accumulated over time.
Metaknowledge concerns a higher level of expertise: un­
derstanding the nature, scope, and limits of our basic, or
primary knowledge. Metaknowledge includes the uncer­
tainty of our estimates and predictions, and the ambigu­
ity inherent in our premises and world views.1

At times, metaknowledge is more important than
primary knowledge. For example, knowing when to see
a lawyer or a doctor (metaknowledge) is more impor­
tant than how much we know about law or medicine
(primary knowledge). We draw on our metaknowledge
when we conclude that we have enough information
and are ready to make a decision now. If we think we
are ready to decide when we are not, we may make cost­
ly mistakes. Only when we appreciate the limits of our
primary knowledge can we sensibly ask for more or bet­
ter information.

E xamining confidence ranges, one of several ways re­
searchers study metaknowledge, is a practical means of
assessing personai uncertainty. Having sound meta­
knowledge means being able to predict within reason­
able ranges.

W hether you should focus on 90 percent, 70 per­
cent, or just 50 percent confidence ranges depends on



r the issues and risks involved. When building a complex We strongly disagree. W hether you know a lot or a little
new oil refinery, where the downside risks are high, you about a subject, you are still responsible for knowing
may want to incorporate even extreme swings in oil how much you don’t know. If you know a lot, as a com-
prices. In that case, perhaps a 95 percent confidence puter industry manager should, your 90 percent confi-
range on future crude oil price levels should be assessed. dence intervals will be narrow; if you know less, they
However, for estimating regional sales levels, you may should be wider. In either case, your subjective 90 per-
want a 50 percent range, as you can cope more easily cent confidence intervals should, by definition, capture
with surprises outside that range. the true answers 90 percent of the time. (IBM had

We and others have found that whether managers are 365,000 employees on 31 December 1990.)
asked for 50 percent, 70 percent, or 90 percent confi- In actuality the job relevance of the questions does
dence ranges, few employees are able to supply them re- affect results, possibly because experience reduces over-
alistically. Even experts, who by definition know a lot confidence. In Table 1 we see that managers in the com-
about a specialized topic, are often unable to express puter firm did better on firm-specific questions (58 per-
precisely how much they do not know. Yet to size up cent misses) than on those covering their entire industry
and factor uncertainty into our judgments is crucial to (80 percent misses). The data-processing managers, too,
successful decision making. Experimental evidence sug- showed less overconfidence on industry-specific items
gestS that this is a serious weakness in human judgment, (42 percent misses) than on general business facts (62
even among those well versed in the use of quantitative percent misses).
tools. In technical language, few people are well calibrat- Although these results suggest that job relevance tends
ed; that is, few people can accurately assess their uncer- to reduce overconfidence, would such a pattern be con-
tainty. In business, this translates into risk underesti- firmed by a more systematic study? Moreover, is the re-
mates, missed deadlines, and budget overruns. duction in overconfidence only partial, or would ques-

T able 1 summarizes some results we have collect- tions very specific to people’s jobs drastically reduce
ed from different industries, most often using ques- overconfidence? We tested this using ninety-six profes-
tions tailored to that industry and occasionally to a sionals drawn from a variety of occupations. We used
specific firm. No group of managers we tested ever two different confidence quizzes: the first contained fif-
exhibited adequate metaknowledge; every group be- teen job-specific questions; the second contained fifteen
lieved it knew more than it did about its industry or questions unrelated to these professionals’ jobs. The un-
company. Of the 2,000-plus individuals to whom we related questions were created by having pairs of profes-
have given a ten-question quiz using 90 percent con- sionals exchange questionnaires. Thus, one person’s job-
fidence inter vals, fewer t h a n 1 p e r c e n t w e r e n o t specific questions become the other’s unrelated questions
overconfident. and vice versa. As a check, we asked everyone afterwards

Our own evidence in Table 1 is confirmed by a large to rate the job relevance of the fifteen questions in the
body of similar results from different -�- �–�—-�————�——��—–�—- —– – – —�-

professions, levels of expertise, and Table 1 Overconfidence across Industries
ages.2 The only cross-cultural studies,
done with Asian managers of several
nationalities, further confirm the
ubiquity of overconfidence.3

If a question falls outside your area
of expertise, should you be excused if
your confidence interval misses it?
W hen we ask, “How many total em­
ployees did IBM have on its payroll
on 31 December 1990?” managers
outside the computer industry some­
times remark that IBM’s staff size is

Industry Tested



Data processing

Money management

Security analysis

Kind of Questions
Used in Test

General business
Industry & firm
Industry & firm

Percentage of Misses

Ideal* Actual

10% 61% 750

50 78 750

5 80 1290

5 58 1290

10 42 252

10 62 261

10 50 480

10 50 850

50 79 850

10 49 390

10 64 497

irrelevant to their job, so they should * The ideal percentage of misses is 1 DO% minus the size of the confidence interval. Thus. a 10% ideal
be forgiven for their poor p e r for- means that managers were asked f o r 90% confidence intervals.
mance on an overconfidence quiz. -=-��e!�ta���rn

be�o��ucl_��e�n�s-ma� _e a_cro:s�persons �n� ��es���=� ____ _


– I

first quiz, using a scale from 1 (irrelevant) to 7 (highly
relevant). For these 90 percent confidence ranges, the
unrelated quiz y ielded 53 percent misses (instead of the
ideal 10 percent). For the job-relevant quiz, rhe percent­
age of misses went from a high of 58 percent for the least
relevant questions to 39 percent for the most relevant
ones. Figure 1 display s rhis downward trend.’ Note that
overconfidence does not vanish, but remains at 29 per­
cent over rhe ideal, even for rhe most relevant questions.

In sum, better primary knowledge is generally associ­
ated with better (though still imperfect) metaknowl­
edge. That is, experts know better what they don’t
know, and this fact is one key to effective solutions, as
we discuss next.

Developing Good Metaknowledge

How might professionals develop a sharper sense of how
much rhey do and do not know? Once the existence of
overconfidence is acknowledged, two elements are es­
sential: feedback and accountability.

Feedback rhat is accurate, timely, and precise tells us
by how much our estimates missed the mark. Account­
ability forces us to confront that feedback, recalibrate
our perceptions about primary knowledge, and temper
our opinions accordingly.

One mistake we often see managers make is equating
experience and learning. Experience is inevitable; learning
is not. Overconfidence persists in spite of experience be-


Figure 1

of Misses

Job Relevance of Questions Partially
Reduces Overconfidence






. 1


Ideal proportion of misses

3 4 5



6 7

cause we often fail to learn from experience.’ In order to
learn, we need feedback about the accuracy of our opin­
ions and doubts. We also need the motivation to trans­
late this information into better metaknowledge.

At least three groups of professionals have used sys­
tematic feedback and accountability to develop excellent
metaknowledge: Shell’s geologists, public accountants,
and weather forecasters.
• Shell’s Geologists. Recall the earlier example of Royal
Dutch/Shell, the Anglo-Dutch oil and gas giant. Shell
had noticed that newly hired geologists were wrong
much more often than their levels of confidence im­
plied. For instance, they would estimate a 40 percent
chance of finding oil, but when ten such wells were ac­
tually drilled, only one or two would produce. This
overconfidence cost Shell considerable time and money.

These judgment flaws puzzled senior Shell execu­
tives, as the geologists possessed impeccable credentials.
How could well-trained individuals be overconfident so
much of the time? Put simply, their primary knowledge
was much more advanced than their metaknowledge.
To develop good metaknowledge requires repeated feed­
back, which was coming too slowly and costing too
much money.

In response, Shell designed a training program to help
geologists develop calibration power. As part of this train­
ing, rhe geologists received numerous past cases rhat in­
corporated the many factors affecting oil deposits. For
each case, they had to provide best guesses as well as
ranges that were numerically precise. Then they were
given feedback as to what had actually happened. The
training worked wonderfully: now, when Shell geologists
predict a 40 percent chance of producing oil, four out of
ten times rhe company averages a hit.
• Public Accountants. When experienced auditors pro­
vided estimates and confidence ranges for account bal­
ances, they actually proved slightly underconfident.6
Their ranges were too wide rather than too narrow. Per­
haps accountants have learned to compensate for over­
confidence because of their role as detectors of fraud
and error. The profession places an extraordinarily high
value on conservative judgments.
• Wearher Forecasters. But what, then, are we to make
of the only other professional group that has been found
to be well calibrated: U.S. Weather Service forecasters?
Figure 2 tells a remarkable story of accurate subjective
probabilities/ W hen U.S. Weather Service forecasters
predicted a 30 percent chance of rain, as they did 15,536
times in this study, it rained almost exactly 30 percent of
the time. This superb accuracy holds along the entire
range of probability, except at the highest levels. When a


I 00 percent chance of rain is predicted, it actually rains
only 90 percent of the time. This prediction error re­
flects deliberate caution on the part of the forecasters.

What these three groups have in common is precise,
timely feedback about repeated judgments in a field
whose knowledge base is relatively stable, unlike the stock
market or fashion industry, for instance. Furthermore, all
three groups are held accountable by their supervisors or
professional colleagues for the accuracy of their confi­
dence judgments. Within a day, the weather forecasters
receive feedback about whether or not it rains, and their
predictive performance is factored into their salary in­
creases and promotions. We believe that timely feedback
and accountability can gradually reduce the bias toward
overconfidence in almost all professions. Being “well cali­
brated” is a teachable, learnable skill.

Organizations can accelerate the slow, costly process
of learning from experience by keeping better track of
managerial judgments and estimates. Performance re­
views should emphasize the value to the firm of realism
and back this emphasis up with both assessments and
incentives. In addition, training programs can provide
feedback on simulated or past decisions whose out­
comes are not widely known, just as Shell’s training pro­
gram did.

Systematic feedback works, even though it treats only

Figure 2 U.S. Weather Service Forecasting Accuracy






Precipitation 50
(%) 40




37303 _ _.__….__-L-_.___.____.___…..�_….J�.L….J
0 60 70 80 90 100

Forecaated Probability of Precipitation
in the Next 24 Hours (%1

Note: N�mbers beside each point indicate sample sizes. Reprinted by
permiSSIOn from A.H. Murphy and R.l. Winkler. “Probability Forecasting
1n Meteorology,” Journal of the American Statistical Association
79 (1984): 489-500.


the symptoms of overconfidence. That is, it corrects
overconfidence without teaching what caused it in the
first place. Several other techniques for reducing over­
confidence directly attack its causes. No single cause or
prototypical situation can be consistently connected
with overconfidence. There are three classes of causes:
cognitive, physiological and motivational.

Cognitive Causes of Overconfidence

• Availability. A major reason for overconfidence in pre­
dictions is that people have difficulty in imagining all
the ways that events can unfold. Psychologists call this
the availability bias: what’s out of sight is often out of
mind.8 Because we fail to envision important pathways
in the complex net of future events, we become unduly
confident about predictions based on the fewer path­
ways we actually do consider.

The limited paths that are evident (e.g., the expected
and the ideal scenarios) may exert more weight on like­
lihood judgments than they should. Bridge players pro­
vide a telling example of how availability can cause over­
confidence.9 More experienced bridge players are better­
calibrated bidders because they take into account more
unusual events or hands. Less experienced players be­
lieve they can make hands they often cannot, precisely
because they fail to consider uncommon occurrences.
• Anchoring. A second reason for overconfidence relates
to the anchoring bias, a tendency to anchor on one value
or idea and not adjust away from it sufficiently.10 It is
typical to provide a best guess before we give a ballpark
range or confidence interval. For example, we usually es­
timate next quarter’s unit sales before we come up with a
confidence range. The sales estimate becomes an anchor
point and drags the high and low brackets, preventing
them from moving far enough from the best estimates.

How strong is the best estimate’s pull? To answer this
question, we presented twenty trivia questions (e.g.,
“What is the length of the Nile River?”) to two groups of
managers. One group (of eighty-four people) first gave a
best estimate, that is, an anchor point, and then provid­
ed a 90 percent confidence interval. The second group
(of fifty-one people) directly supplied a confidence range
without ever committing to a best guess. The first group
scored 61 percent misses (compared to the ideal of 10
percent). In contrast, the unanchored group’s intervals
were wider and missed only 48 percent of the true an­
swers. Thus, overconfidence was reduced substantially by
simply skipping best guesses and moving directly to
ranges. (The Nile River is 3,405 miles long.)

Although this de-anchoring technique has yet to be


verified outside the controlled laboratory, we see no rea­
son why it should not work as well in managerial envi­
ronments. Interestingly, how well it works will depend
on managers’ ability to focus on the confidence interval
and block out of their thinking any earlier estimate that
might serve as an anchor.
• The Confirmation Bias. A third cognitive reason for
overconfidence concerns our mental search process.
When making predictions or forecasts, we often lean to­
ward one perspective, and the natural tendency is to
seek support for our initial view rather than to look for
disconfirming evidence. Unfortunately, the more com­
plex and uncertain a decision is, the easier it is to find
one-sided support. Realistic confidence requires seeking
disconfirming, as well as confirming, evidence.”

How much weight to give to evidence, pro or con, is
a complex issue depending both on the strength of the
evidence itself and on the credibility of its source.
Griffin and T versky, for instance, suggest that people
over-weight the strength of evidence (e.g., how well a
candidate did in an interview) relative to the credibility
of that type of evidence (the limited insight gained from
any single interview).12 Whenever source credibility is
low, as is often the case in business, and the strength of
the evidence is highly suggestive, overconfidence is like­
ly to occur. Thus, the interviewed candidate is too con­
fidently predicted to be a winner or loser, given the falli­
ble, limited evidence obtainable from a short, one-time
interview. Ironically, Griffin and T versky predict under­
confidence under reverse circumstances, when the credi­
bility of the source is high, but the evidence does not
point strongly to one action or opinion.
• Hindsight. Hindsight makes us believe that the world
is more predictable than it really is. What happened
often seems more likely afterwards than it did before­
hand, since we fail to appreciate the full uncertainty that
existed at the time. Recall, for instance, George Bush’s
landslide victory over Michael Dukakis in 1988 (54 per­
cent of the popular vote). At the time of the nominating
conventions, the outcome of the election seemed far
from certain. Indeed, University of Chicago MBA stu­
dents that summer gave Bush only a 49 percent chance
of winning,u and the political press frequently cited the
“wimp” factor. Nonetheless, the results might seem
quite predictable some years later. Hindsight instills an
illusion of omniscience.

Cognitive Remedies to Overconfidence

What remedies are available for cognitive sources of
overconfidence? We look at five techniques.


• Accelerated Feedback. Recall how successful Shell was
in training its junior geologists on past cases where the
outcome was known, so they could get immediate feed­
back. Simple experiments with trivia questions have
demonstrated the efficacy of accelerated feedback.’4 This
kind of training can be especially effective for new em­
ployees. Using tests derived from actual company
records, employees could be trained to estimate their
confidence in knowledge relevant to their new jobs. At
first, these predictions will almost certainly be overcon­
fident, but good feedback will quickly reduce it. And, in
contrast to learning from experience, which tends to be
slow and expensive, good feedback will reduce overcon­
fidence cheaply.

Bur what can you do when faced with a single deci­
sion that you must make soon? Try to improve your
thinking by bringing to mind relevant considerations
that might easily be overlooked. The next four tech­
niques offer specific methods for doing so.
• Counterargumentation. Think of reasons why your
initial beliefs might be wrong, or ask others to offer
counterarguments. Several studies have demonstrated
the power of generating counterarguments, including
one where a major company tested its managers by ask­
ing questions such as the following:”

Our company’s current liabilities (defined as notes
payable, short-term loans, etc.) were $1,911 million
and $1,551 million as of December 31, 19xx, and
March 31, 19xx, respectively. For October 31, 19xx,
the company’s current liabilities will be (circle one):

(a) greater than $1,900 million

(b) less than or equal to $1,900 million

Give your subjective probability that you will be
correct:__ %.

Half of the participants in the experiment were merely
asked to circle (a) or (b) and then state how confident
they were about their choice. This group’s mean estimat­
ed probability of being correct was 72 percent. However,
they actually picked the correct answer only 54 percent of
the time. Hence, they were overconfident by 18 percent.
The other half of the participants were asked to think of
“the major reason why the alternative circled might be
wrong” before giving their subjective probabilities. That
is, they were asked to think disconfirmingly and provide
at least one counterargument to their initial guess. Then
they were allowed to change their answer if they wished.
This group’s average estimated probability of being


correct was 73 percent, and they actually picked the cor­
rect answer 62 percent of the time. Thus, their level of
overconfidence was only 11 percent, a reduction of nearly
two-fifths thanks to a single counterargument. Other studies
have found that, when listing pros and cons, the cons do
the most good in countering overconfidence.'(,

But is this practical? It depends. We see no reason
why major capital budgeting requests could not have a
counterargument section in which managers are asked
to identify the major reasons not to go ahead. And if the
project does fail later, the actual causes had better be
listed in this contrarian section of the report. A warning,
however. To be useful, this process must be taken seri­
ously, with serious consequences (both good and bad)
for the managers involved. Otherwise it may degenerate
into a useless formality or, worse, corporate “gaming”
(i.e., managers withholding their genuine concerns in
favor of saying whatever gets the budget approved). A
second problem is that managers may truly not recog­
nize potential hazards. The next tactic, Paths to Trouble,
addresses that problem.
• Paths to Trouble. If we are overconfident in predict­
ing success because we cannot see the paths to potential
trouble, fault trees may help. A fault tree is a hierarchi­
cal diagram designed to help identify all the paths to
some specific “fault” or problem. For an example, see
Figure 3. To be useful, fault trees must be reasonably
complete, at least in identifying the major categories of
potential trouble. If they are not, chances are that even
specialists will fail to realize what is missing. 17 People as­
sume the causes listed account for almost every thing
that could go wrong and underestimate the final catego­
ry, “all other” causes of failure. In the study that used
the restaurant fault tree, the “all other” category was es­
timated by hospitality industry managers to contain
only 7 percent of the chance that something might go
wrong, whereas it really contained 54 percent of the
chance. 1M

How can this blindness be overcome? Warning peo­
ple does not seem to help.19 What does work is asking
people to extend the fault tree by listing additional caus­
es of the problem. 20 In the restaurant study, some people
were shown branches with only six instead of twelve
causes and asked to provide further reasons. When two
more causes were listed by managers themselves, the
original omission error of 46 percent dropped to 23
percent; when four were listed, it dropped to 12 per­
cent; and when six possible causes were added, it disap­
peared entirely. In sum, the more causes generated, the
smaller is the error of assuming that all relevant causes are
already listed.


• Paths to the Future. If deeper thinking is called for,
bey ond the listing of reasons, explicit scenario analysis
may be useful. Whereas fault trees highlight individual
causes, scenarios focus on their conjunction. Scenarios
are script-like narratives that paint in vivid detail how
the future might unfold in one or another direction.
Envisioning vastly different worlds than those expected
has helped companies like Royal Dutch/Shell to better
estimate economic and political uncenainty.2′ A direct
test that compared 90 percent confidence intervals be­
fore and after scenario construction found, on average, a
30 percent stretching of ranges. 22 Asking managers to
construct different scenarios makes them better appreci­
ate the uncertainty in key parameters or estimates. In
addition, it often provides new ideas for innovation or
competitive positioning.
• Awareness Alone. Although these techniques are valu­
able, we happily acknowledge that, for many managers,
awareness alone may be all that is needed. Good man­
agers often devise their own solutions to the problems
of overconfidence.

Recall the head loan officer who was certain that he
and his staff knew their competitors quite well. Mter
failing the tailored overconfidence quiz, he took im­
mediate action. Each officer was required to con­
tribute information to a “competitor alert” file. And
each was required to check the file weekly to gain a
m o r e realistic a p p r e c i a t ion of their competition.
Within three weeks, a loan officer found information
in the file signaling that a major client was contem­
plating a shift to another bank. The competitor was
not one of the city’s major commercial banks, and the
magnitude of the business at risk exceeded the com­
petitor’s legal lending limit. However, by joining with
another institution, it was able to offer a loan large
enough to meet the client’s needs. Thus alerted, the
loan officer in charge of the account convinced the
client not to switch banks, saving $160,000 in annual

The head of sales for Index Technology took a differ­
ent approach when his salespeople were overconfident
about if and when orders of the company’s product
would be written by potential customers. He called some
customers himself. His salespeople didn’t like it, but the
approach worked: soon they were predicting orders and
the timing of those orders much more accurately.

A negotiation experiment further underscores the
value of awareness alone. 2·’ Subjects believed they had a
65 percent chance of winning in a simulated negotia­
tion task entailing binding arbitration. In this set-up,
for every winner there had to be a loser, implying a 50

Russo & ScHOEMAKER 13

Figure 3 Fault Tree for Restaurant Failure

I Restaurant failure due to decreasing profits j


Decreasing revenues I Increasing costs I


Decreasing number Decreasing average Increasing food costs Increasing labor costs Increasing overhead

of customers food/beverage check cost

•Incorrect pricing • Decreasing perceived • Improper purchasing and • Increasing overtime • High debt service cost

• Unclear image of value by customers receiving scheduling • Poor facility design

property • Incorrect pricing • Menu variety !too limited • Decreasing productivity • High occupancy costs

• Changing atmosphere • Inadequate service pace or too extensive) • Poor organizational •Inadequate capital

• Outdated restaurant • Poor merchandising • Poor sales forecasting climate structure

concept • Lack of employee • Changes in supplier • Union rules • Low sales volume

• Inadequate promotion motivation market • Improper physical layout •Improper growth rate

and advertising • Changing customer • High waste and leftovers and equipment • High administrative costs

• Changing customer dining out budget • Inadequate number of • Poor employee selection • Incorrect work method of

expectations • Changing mix of food/ employees and training personnel

• Poor food quality beverage sales • High theft • High employee turnover •Improper business hours

• Changing competition •Inconsistent food quality • Poor supervision • Menu variety too • Outdated facility and

• Poor service quality • Changing competition •Incorrect size of food extensive equipment
• Changing customer • Improper portion size portions • Poor supervision • High inflation

demographics • Changing customer • Improper storage and • Inadequate wage • Poor credit rating
• Lack of menu variety demographics issuing structure •All other
• Changing location • Changing customer •All other •Inefficient employee

characteristics tastes scheduling
• All other • All other • Increasing benefit costs

• All other

Reprinted by permission of John Wiley & Sons. Ltd .• copyright 1988. From l. Dube-Rioux and J.E. Russo, “An Availability Bias in Professional Judgement,”
Journal of Behavioral Decision Making 1 11988): 223-237.

percent chance of winning. Hence, most of the people
were overconfident. The researchers then took a ran­
dom half of the people aside and warned them about
overconfidence. Compared to the unwarned group,
those forewarned were 30 percent more likely to reach
a negotiated agreement instead of having to turn to
costly arbitration, and they achieved net dollar benefits
that were 70 percent higher. As skilled lawyers know,
negotiating a settlement is one area where realism

General Versus Specific Awareness

Although general awareness of a bias is invaluable, it does
not guarantee that the bias will be spotted in every in­
stance. Consider this study. Twelve financial officers
were asked to estimate ten quantities pertinent to their


organization’s business operations and to provide a 90
percent confidence interval for each.24 As usual, these in­
tervals failed to capture the true value a high percentage
of the time; in this case, the failure rate was 78 percent
versus the ideal of 10 percent. In addition, the financial
officers were asked to estimate ” how many of the ten in­
tervals you gave … will contain the actual value.” This
was asked immediately after the ten intervals were con­
structed and before the true answers were revealed. Of
course, every officer should have answered “nine” be­
cause that is what a 90 percent confidence interval
means, by definition. However, only one did. The oth­
ers estimated that fewer than nine of the ten intervals
would capture the true value. On average, the twelve of­
ficers guessed that they would miss 5.6 of the ten ques­
tions but couldn’t tell which ones. These data suggest
that people are more aware of overconfidence in general


than they are in particular.
The same problem – of general awareness but specif­

ic blindness – was described by John Stuart Mill, the
19th century economist and social philosopher, in On

Unfortunately for … mankind, the fact of their fal­
libility is for from carrying the weight of their practi­
cal judgment, which is always allowed to it in theo­
ry; for while everyone knows himself to be fallible,
few . . . admit the supposition that any opinion of
which they feel very certain may be one of the exam­
ples of the error to which they acknowledge them­
selves to be liable.

Physiological Causes of Overconfidence

Because overconfidence is a distortion of judgment, it is
often thought of as a purely mental phenomenon; how­
ever, at times it has biochemical causes. Euphoria, the
elated feeling of well-being that commonly follows per­
sonal or professional success, may cause overconfidence.
(The biochemical compounds involved in euphoria ap­
pear to be hormones, such as adrenalin and endor­
phines, that the body produces as a response to strong
emotional reactions.) We also suspect that drugs like co­
caine and alcohol can produce overconfidence. 25

Ford Motor Company provides an example of how
a major firm dealt with the negative side effects of eu­
phoria. As the 1970s ended, Ford faced hard times: re­
duced market share, layoffs, and the superior quality
of Japanese cars. In response to these conditions, Ford
organized meetings of its plant managers and assistant
managers to solicit and communicate suggestions for
improving manufacturing quality. The flood of ideas,
and the resulting enthusiasm for what might be ac­
complished, s w ept n e a r l y e v e r yone a way. W i s e l y,
Ford’s top management imposed a cooling-off period
of several weeks before the returning managers could
implement any of the suggestions. Because of the eu­
phoric mood at the end of the meetings, senior execu­
tives distrusted their managers’ judgment, and they
wanted time for a more calculated look prior to com­
mitting major funds.

Dealing successfully with physiological causes of
overconfidence, as with all types of overconfidence, re­
quires awareness of the problem: you can’t fix it if you
can’t find it. In this regard, individual awareness is the
single most important factor. If you are euphoric, wait


to commit yourself to a plan of action, just as, if you
drink, don’t drive.

Overconfidence in Group Judgments

By this time, you may wonder if groups do better than
individuals when sizing up uncertainty. The answer is
mixed.26 Group judgments can be better than individual
ones, precisely because in groups people are forced to
recognize that others see the world differently than they
do. This often sparks a realization that perhaps their
own views are held with unjustifiable conviction. A t
other times, however, groups may bolster the majority
opinion to even more extreme levels.

To test group overconfidence, we conducted a simple
experiment with eighty-three managers. First, people
were asked to privately form 90 percent confidence
ranges on ten questions. Then they were asked to com­
pare and discuss their ranges in groups of three or four
in order to come up with a single group range for each
question. We did not specifY how this was to be accom­
plished. Some groups argued heatedly; others merely av­
eraged the individual guesses; still others used the most
extreme values in the group as their outer brackets.
After the group decisions were made, people were al­
lowed to change their private ranges. The initial, unre­
vised private judgments generated an average of 72 per­
cent misses (compared to an ideal of 10 percent),
signifying serious overconfidence. Group judgments
were significantly less overconfident, with 56 percent
misses on average. The revised private judgments made
after the group decisions resulted in 62 percent misses.

Making Group Judgments Better

On average, the group judgments were better than indi­
vidual judgments in the above task. At worst, they forced
a compromise; at best, they encouraged openmindedness.
Individually, however, people may still anchor too strong­
ly to their initial view and return to it when given the
chance. This stubbornness can be to their, and their com­
pany’s, detriment. There are relatively simple techniques
for minimizing this recidivist tendency.

Delphi techniques and other procedures for sharing
and averaging opinions are especially feasible in a net­
worked PC environment. Rather than go around the
table, collect people’s initial estimates and ranges pri­
vately. Next, share these ranges and only then com­
mence a debate. Mter group discussion, ask for one


more round of opinions and run with those averages.
An extensive literature exists on expert aggregation and
averaging. as well as experimental work on individual
versus group calibration.!-

Motivational Factors in Overconfidence

Overconfidence isn’t all bad! One legitimate cause of
overconfidence is our need to believe in our abilities.
Indeed, confidence in one’s abilities is particularly wide­
spread. At the beginning of a course, we often ask our
MBA students (anonymously) whether their final grade
will be in the bottom or top half of the class. The great
majority are certain they will finish in the top half, and
they are willing to bet on it.

Many of these people are distorting reality, yet their
optimism has motivational value. Would risky projects
be undertaken if a few key people did not have an unre­
alistic belief in their chances of success? As Goethe
wrote, “For a man to achieve all that is demanded of
him he must regard himself as greater than he is.”

If the motivating value of overconfidence is clear, so
is its downside. The value and danger of overconfidence
may especially conflict for entrepreneurs. They often
take risks others would not, and they must persuade in­
vestors and employees to join them in highly uncertain
endeavors. Yet their eventual success also requires real­
ism. A partner at a venture capital firm summarized this
problem: “You expect entrepreneurs to have … an un­
shakable sense that they absolutely cannot fail. Yet since
we will be partners with these people, we want to be
sure that their egos will not stand in the way of making
the best decisions for the business.”

Moreover, to succeed in many business endeavors, we
have to project confidence even when it cannot be justi­
fied. Because people often equate confidence with com­
petence, you had better sound confident if you want
your opinions to be treated as credible. It is seldom easy
to stand up at an important meeting and say, ”I’m not
sure.” Instead, people go out on a limb.

Can anything be done to reconcile the danger of dis­
torting reality with the value of optimism? Perhaps the
best advice is: Don’t fool yourself. Don’t permit yourself
to be overconfident when making important decisions
or commitments.

Deciding and Doing

We believe that much of the damage can be avoided if
managers distinguish between deciding and doing.
Deciding requires realism. But in implementing the


decision, the motivational benefits of overconfidence
frequently outweigh its dangers.

Separating deciding from doing is not simple, and it’s
harder than it used to be. A century ago, business orga­
nizations were more like vertical pyramids: the deciders
were on top and the workers were underneath. But in
today’s “flattened” organizations, every manager is both
a decider and a doer.

So what should today’s managers do? Our recom­
mendation is to be aware of when you are fUnctioning as a
decider and when your primary role is that of doer, motiva­
tor, or implementer. When you are deciding, be realistic,
both about how much you know and how much you
don’t know. W hen you are implementing, indulge in
overconfidence when, and if, it is valuable to your per­
formance or that of others.

All of us need self-confidence to function. We might
not show up for work every morning if we did not believe
we could make a difference. Nevertheless, too much con­
fidence can backfire – can cause us to bet on plans, peo­
ple, or projects which a more realistic appraisal would
have rejected. Though normally an advocate of rational
calculation, Lord Keynes keenly observed this human
dilemma: “A large proportion of our positive activities de­
pend on spontaneous optimism rather than on mathe­
matical expectation … if animal spirits are dimmed and
the spontaneous optimism falters, leaving us to depend
on nothing but mathematical expectation, enterprise will
fade and die.”+


The authors acknowledge janet Sniezek and Ilan Yaniv for their construc­
tive comments and jack B. Williams for his editorial advice.
I. Linguists distinguish between language competence (the ability to
produce coherent statements) and metalanguage (the ability t o state
the rules of the language). Such a clear distinction does not always
exist between primary knowledge and metaknowledge. Early in the
century, U.S. Weather Service forecasters simply predicted whether or
not it would rain (a statement of their primary knowledge). Now they
provide an explicit probability of rain, making uncertainty assessment
an explicit part of their primary knowledge.
2. S. Lichtenstein, B. Fischhoff, and L.D. Phillips, ”Calibration of
Probabilities: The State of the Art to 1980,” in judgment under
Uncertainty: Heuristics and Biases, eds. D. Kahneman, P. Slovic, and A.
Tversky (New York: Cambridge University Press, 1982), pp. 306-
3. G.N. Wr i g h t a n d L.D. P h i l l i p s , “C u l t u r a l Va r i a t io n s i n
P r o b a b i l i s t i c Thinking: A l t e r n a t i v e W a y s o f D e a li n g w i t h
Uncertainty,” lnternationaljoumal o f Psychology 1 5 (1980): 239-257.
4. All claims made about differences or trends are statistically signifi­
cant at the .05 level or lower. The sample sizes for the percentages in
Figure 1 range from a low of 122 when relevance = I to a high of 270
when relevance= 7, with the unrelated percentage based on all 1,440
unrelated questions.


5. J.E. Russo and P.J.H. Schoemaker, Decision Traps (New York:
Simon and Schuster, 1990).
6. L.A. Tomassini et al., “Calibration of Auditors’ Probabilistic
Judgments: Some Empirical Evidence,” Organizational Behavior and
Human Pnfonnance 30 ( 1982): 391-406.
7. A.H. Murphy and R.L. Winkler, “Probability Forecasting in
Meteorology,” journal of the American Statistical Association 79 ( 1984)
8. A. Tversky and D. Kahneman, “Availability: A Heuristic for
Judging Frequency and Probability,” Cognitive Psychology 4 (1973):
B. Fischhoff, P. Slovic, and S. Lichtenstein, “Fault Trees: Sensitivity of
Estimated Failure Probabilities to Problem Representation,” journal of
Experimental Psychology: Human Perception and Pnformance 4 ( 1978):
9. G. Keren, “Facing Uncertainty in t h e Game of Bridge: A
Calibration Study,” Organizational Behavior and Human Decision
Processes 39 ( 1987): 98-114.
10. P. Slovic and S. Lichtenstein, “Comparison of Bayesian and
Regression Approaches to the Study of Information Processing in
Judgment,” Organizational Behavior and Human Performance 6
(1971): 641-744;
A. Tversky and D. Kahneman, “Judgment under Uncertainty:
Heuristics and Biases,” Science 185 (1974): 1124-1131.
II. J. Klayman and Y.W. Ha, “Confirmation, Disconfirmation, and
Information in Hypothesis Testing,” Psychological Review 94, 2
(1987): 211-228.
12. D. Griffin and A. Tversky, “The Weighing of Evidence and the
Determinants of Confidence” (Waterloo, Ontario: University of
Waterloo, working paper, 1991).
13. P.J.H. Schoemaker, “Scenario Thinking” (Chicago: Graduate
School of Business, University of Chicago, working paper, 1991).
14. For a review, see Lichtenstein, Fischhoff, and Phillips (1982).
15. J. Mahajan and ).C. Whitney, Jr., “Confidence Assessment and
the Calibration of Probabilistic Judgments in Strategic Decision
Making” (Tucson: University of Ariwna, working paper series #12,
16. S.J. Hoch, “Availability and Inference in Predictive Judgment,”
Journal of Experimental Psychology: Learning. Memory, and Cognition
I 0 (1984): 649-662.
17. Fischhoff, Slovic, and Lichtenstein (1978).
18. L. Dube-Rioux a n d J.E. R u sso, “An Availability Bias i n
Professional Judgment,” journal of Behavioral Decision Making I
(1988): 223-237. In this study, six of the twelve listed causes in a
branch of a fault tree (see Figure 3) were removed. If people, in this
case hospitality industry managers, were properly aware of all the
major causes, then all of the probability of these six unlisted causes
should show up in the last, “all other” category. In fact, very little did,
strongly suggesting that what is out of sight is out of mind; i.e., the
availability bias operates.
19. Fischhoff, Slovic, and Lichtenstein (1978).
20. Dube-Rioux and Russo (1988).
21. P. Wack, “Scenarios: Uncharted Waters Ahead,” Harvard Business
Review, September-October 1985, pp. 73-89;
P. Wack, “Scenarios: Shooting the Rapids,” Harvard Business Review,
November-December 1985, pp. 139-150.
22. Schoemaker (1991).
23. MA. Neale and M.H. Bazerman, “The Effects of Framing and
Negotiator Overconfidence on Bargaining Behavior and Outcomes,”
Acadnny ofManagemmt journal28 (1985): 34-49.


24. ).A. Sniezek and T. Buckley, “Level of Confidence Depends on
Level of Aggregation,” journal of Behavioral Decision Making4 (1991):
25. We wonder how many traffic fatalities are caused by alcohol-in­
duced overconfidence. Ccnainly driving skills are impaired by alcohol.
but this may be only part of the story. A more deadly aspect is that the
drinker’s confidence is not reduced nearly as much as the ability itself.
This confidence gap between the skill levels drivers believe they possess
and the reduced levels they actually have seems to be a primary prob­
lem with drunk drivers.
26. Despite a presumption that “two heads are better than one,”
groups do not always make better decisions than individuals. The phe­
nomenon known as groupthink is one serious problem. Whether
groups are superior seems to depend on whether conflict is articulated
or swept under the rug. See:
I.L. Janis, Groupthink, 2nd ed. (Boston: Houghton Mifflin, 1982.)
27. R.T. Clemen and R.L. Winkler, “Unanimity and Compromise
among Probability Forecasters,” Management Science 36 (1990): 767-
779; and
)A. Sniezek and RA. Henry, “Accuracy and Confidence in Group
) udgment,” Organizational Behavior and Human Decision Processes 43
(1991): 1-28.

The answers to the Confidence Quiz: (I) 96,727 patents; (2) 111
Japanese corporations; (3) 59,130,007 arrivals and depanures; (4)
2,076,713; (5) 67,496 degrees; (6) 6,700 deaths; (7) 5,757 miles; (8)
77.8 million units; (9) 147,324 automobiles; and (10) $354 billion.

Reprinted by Permission Sloan Management Review e 1992.

Reprint 3321


No more than 600 words

Write-up Topic 3: Consider the following fact: 80% of small businesses fail within the first three years. A friend is unaware of this fact, and wishes to quit his consulting job and open a restaurant in Los Angeles (or pick another city you’re more familiar with). As a good friend, you want to be sure that overconfidence is not playing a significant role in his or her decision. Compose a letter/email to your friend to help him or her navigate the pitfalls of overconfidence. In writing this letter, make sure you address the following (and skip the letter intro and conclusion).

1. Why might your friend be overconfident about the success of this venture?

2. What are the most important sources of his/her overconfidence?

3. Which of these sources do you believe will produce the most resistance to your persuasion?

HINT: It’s not sufficient to just tell your friend they are being overconfident!

Place your order
(550 words)

Approximate price: $22

Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
The price is based on these factors:
Academic level
Number of pages
Basic features
  • Free title page and bibliography
  • Unlimited revisions
  • Plagiarism-free guarantee
  • Money-back guarantee
  • 24/7 support
On-demand options
  • Writer’s samples
  • Part-by-part delivery
  • Overnight delivery
  • Copies of used sources
  • Expert Proofreading
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.

Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

Read more

Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

Read more

Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

Read more

Privacy policy

Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.

Read more

Fair-cooperation guarantee

By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.

Read more
colle writers

Order your essay today and save 30% with the discount code ESSAYSHELP