Critical Analytical Thinking Part II: Heuristics and Biases Dr. Abdelghani Es-Sajjade [email protected] Overview The law of small numbers Cause and chance Anchors Availability heuristic The public and the experts Representativeness Causal stereotypes Regression to the mean A two-systems view of regression The law of small numbers Observations The counties in which the incidence of kidney cancer is lowest are mostly rural, sparsely populated in the Midwest, the South, and the West Why? The clean living of the rural lifestyle. No air pollution, no water pollution, fresh food without additives. Observations The counties in which the incidence of kidney cancer is highest are mostly rural, sparsely populated in the Midwest, the South, and the West Why? Poverty of rural lifestyle—no access to good medical care, too much alcohol, too much tobacco. Our mind & statistics Explanation has nothing to do with rural life System 1 excels in one form of thinking: it automatically and effortlessly establishes causal connections between events… even when supporting data is minimal or totally absent We are insensitive to sample size or reliability of data. Sample of 150 or 3000, who cares? Why? WYSIATI and system 1 is gullible. Our mind & statistics We know about sample size! But often can’t help ourselves. Did you initially notice “sparsely populated”? What is the difference? Large samples are more precise than small samples. Small samples yield extreme results more often than large samples do. Hence, small counties, less people so …? Certainty & doubt Our mind has a preference for sliding into certainty over maintaining doubt System 1: rich image with poor evidence Even in science: Small sample experiment, complex phenomenon. Exercise 1 Cause & Chance We have an inclination to causal thinking Statistics is different because it focuses on what could have happened instead The null-hypothesis Randomness sometimes appears as a pattern Hot hand: 3 or 4 scores in a row basketball hot hand, team of players who scores 3 or 4 times in a row is now given more passes and extra defended. Research: this sequence of successes and missed shot fits all the conditions of random. The hot hand is in the eye of the beholder. Massive and widespread cognitive illusion. 11 Speaking of the Law of Small Numbers “Yes, the studio has had three successful films since the new CEO took over. But it is too early to declare he has a hot hand.” “The sample of observations is too small to make any inferences. Let’s not follow the law of small numbers.” “I plan to keep the results of the experiment secret until we have a sufficiently large sample. Otherwise we will face pressure to reach a conclusion prematurely.” Anchors Anchoring effect: considering a particular value from an unknown quantity before estimating that quantity  Question: was Ibn Taymiyyah younger or older than 114 years old when he passed away? What is the anchor? 114 years old. You.

1. Critical Analytical Thinking
Part II: Heuristics and Biases
Dr. Abdelghani Es-Sajjade
[email protected]
Overview
The law of small numbers
Cause and chance
Anchors
Availability heuristic
The public and the experts
Representativeness
Causal stereotypes
Regression to the mean
A two-systems view of regression
The law of small numbers
Observations
The counties in which the incidence of kidney cancer is lowest
are mostly rural, sparsely populated in the Midwest, the South,
and the West
Why? The clean living of the rural lifestyle. No air pollution,

2. no water pollution, fresh food without additives.
Observations
The counties in which the incidence of kidney cancer is highest
are mostly rural, sparsely populated in the Midwest, the South,
and the West
Why? Poverty of rural lifestyle—no access to good medical
care, too much alcohol, too much tobacco.
Our mind & statistics
Explanation has nothing to do with rural life
System 1 excels in one form of thinking: it automatically and
effortlessly establishes causal connections between events…
even when supporting data is minimal or totally absent
We are insensitive to sample size or reliability of data.
Sample of 150 or 3000, who cares?
Why? WYSIATI and system 1 is gullible.
Our mind & statistics
We know about sample size!
But often can’t help ourselves.
Did you initially notice “sparsely populated”?
What is the difference?
Large samples are more precise than small samples.
Small samples yield extreme results more often than large

3. samples do.
Hence, small counties, less people so …?
Certainty & doubt
Our mind has a preference for sliding into certainty over
maintaining doubt
System 1: rich image with poor evidence
Even in science:
Small sample experiment, complex phenomenon.
Exercise 1
Cause & Chance
We have an inclination to causal thinking
Statistics is different because it focuses on what could have
happened instead
The null-hypothesis
Randomness sometimes appears as a pattern
Hot hand: 3 or 4 scores in a row
basketball hot hand, team of players who scores 3 or 4 times in
a row is now given more passes and extra defended. Research:
this sequence of successes and missed shot fits all the

4. conditions of random. The hot hand is in the eye of the
beholder. Massive and widespread cognitive illusion.
11
Speaking of the Law of Small Numbers
“Yes, the studio has had three successful films since the new
CEO took over. But it is too early to declare he has a hot hand.”
“The sample of observations is too small to make any
inferences. Let’s not follow the law of small numbers.”
“I plan to keep the results of the experiment secret until we
have a sufficiently large sample. Otherwise we will face
pressure to reach a conclusion prematurely.”
Anchors
Anchoring effect: considering a particular value from an
unknown quantity before estimating that quantity
Question: was Ibn Taymiyyah younger or older than 114 years
old when he passed away?
What is the anchor? 114 years old.
Your answer will be higher than 35 as an anchor.
Asking price: 1,500,000
How much should you pay for a house? You will be influenced
by its asking price.
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Anchoring effect
Produced by 2 processes:

5. Deliberate process of adjustment: System 2.
Automatic priming effect: System 1.
Process 1: effortful operation, often insufficient
Anchoring as adjustment
Process 1: effortful operation, often insufficient
You stop at the near edge of uncertainty…
…when you are no longer sure you should continue
People adjust less when there’s depletion of mental resources
They stay closer to the anchor
Anchoring as priming
EXPERIMENT
(Mussweiler and Strack)
Is the average temperature in Germany higher or lower than 20
degrees?
Is the average temperature in Germany higher or lower than 5
degrees?
All participants were then shown distorted words that they had
to identify.
Results:
Those who were asked 20 degrees had less difficulty
remembering summer words (sun, beach)
Those asked 5 degrees had less difficulty remembering winter
words (frost, ski).

6. Conclusion: anchors call for information that is
compatible/suitable
The Anchoring Index
Is the tallest redwood more or less than 1200 or 180 feet?
Difference between anchors = 1020 feet
Difference in means between two groups of participants was
562 feet
Anchoring index = 562/1020 = 55%
EXPERIMENT
Real estate agents were asked to visit a property and study
extensive booklet with information
2 groups: lower and higher price in booklet.
What is the value of this property?
Anchoring index = 41%
How did you come up with that value?
Not influenced by anchor, with pride. Expertise.
Anchor index for business students with no real estate expertise
and who admitted to be influenced by anchor: 48%
Conclusion: anchors also/even influence experts

7. Exercise 2
USES AND ABUSES OF ANCHORS
We are much more receptive to suggestions than we think…
…and there are people and organizations who know this and
exploit this.
EXPERIMENT
Supermarket in Sioux City, Iowa.
Promotion! Campbell's Soup.
Some days “Limit 12”
Other days no limit.
Average purchased 7 versus 4
23

8. Dealing with anchors
Negotiating a price over purchase of a home
Or even in a shop, advice: walk out if the amount is too much,
don’t use the proposed anchor.
Galinsky and Mussweiler -> use system 2.
Focus on the minimal (but fair) offer he would accept
Focus on the costs of the seller failing to reach an agreement.
What does he have to lose?
Anchoring and the 2 systems
Anchoring, judgement and choice were thought of as system 2.
System 2 uses data retrieval from memory, retrieval is
automatic process by system 1.
System 2 is subject to the biasing influence that makes some
information easier to retrieve than others and has no control
over this.
That's why denial: “I cannot have been influenced by such
absurd information.”
Some lessons on anchoring
Main lesson of priming research: our thoughts and behavior are
influenced by the environment of the moment much more than
we want or know
Many people find the results uncomfortable…
…because threat to agency, autonomy and sense of expertise
and professional pride.
Any number on the table has an anchoring effect on you.
When the stakes are high, mobilize system 2 to counter the
effect.

9. Speaking about anchoring
“The firm we want to acquire sent us their business plan, with
the revenue they expect. We shouldn’t let that number influence
our thinking. Set it aside.”
“Plans are best-case scenarios. Let’s avoid anchoring on plans
when we forecast actual outcomes. Thinking about ways the
plan could go wrong is one way to do it.”
The science of availability
What is definition of heuristic again?
Availability heuristic.
What do people do when they're asked about the frequency of an
event
What is the percentage of people getting divorced after 40?
How many Cubans are there in KSA?
Instances of the category will be retrieved from memory
If retrieval was easy then frequency high if not then low.
Heuristic: simple procedure that helps find an adequate though
imperfect answer to a difficult question. Mental shortcuts
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Exercise 3
Availability heuristic
Availability heuristic substitutes one question for another.
Frequency versus ease with which impressions come to the

10. mind.
Problematic! Bias! Why?
Other factors that influence ease of retrieval:
Media attention
Dramatic event
Personal experiences more powerful than what happens to
others, statistics or mere words.
, salient events related to celebrities or politicians. E.g. You
may exaggerate the number of Hollywood drug addicts.
: plane crash with extensive coverage changed your perception
of safety.
30
Resisting bias
Resisting this large collection of biases is onerous.
Makes you tired.
The chance to avoid a costly mistake is worth the effort.
Awareness of biases contributes to peace in marriages
and probably in other collective projects.
Experiment
Spouses were asked.
How large was your personal contribution to keeping the place
tidy in percentages? Divided in different tasks e.g. Taking out
the trash etc.
Should add up to 100%.
Own contributions more than 100%,
Both spouses remembered their own contributions more than
those of the other.

11. THE PSYCHOLOGY OF AVAILABILITY
Experiment (Norbert Schwarz)
Research question: how will people's impression of a frequency
be affected if they have to list a specific number of instances?
First: list six instances in which you behaved assertively.
Next: evaluate how assertive you are.
Other group were asked to list 12 instances.
Would you think you were more or less assertive?
Schwarz thought that this impression of your own assertiveness
would be affected by:
The number of instances retrieved
Or the ease with which they come to mind
Results: people who had listed only 6 rated themselves as more
assertiveness than those who (struggled more) to come up with
12.
Counterintuitive because 12 is greater than 6.
I'm having this much difficulty coming up with instances I can't
be very assertive."
33
Other experiments
People believe that they use their bicycles less often after
remembering many rather than few instances.
People are less confident in a choice when they are asked to
generate MORE arguments to back it.
People are less confident that an event could have been avoided
after listing more ways it could have been avoided.

12. Are less impressed by a car after listing many of its advantages.
Experiment
Student feedback
Professor at UCLA asked student for different ways to improve
the course.
Two groups: one had to come up with a lower and one with a
higher number of improvements.
The group students who were asked to come up with a higher
number of improvements rated the course higher.
Who can explain?
Circumstances producing bias
When they are engaged in another effortful task at the same
time
When they are in a good mood because they just thought of a
happy episode in their life
If they are knowledgeable novices on the topic of the task, in
contrast to true experts
If they are (or are made to feel) powerful
“I don’t spend a lot of time taking polls around
the world to tell me what I think is the right way
to act. I’ve just got to know how I feel.”
George W. Bush, November 2002

13. Exercise 4
AVAILABILITY AND AFFECT
Affect heuristic: instead of "what do I think a
do I feel about it?“
Affect heuristic simplifies our world and makes it more
organized and consistent than reality.
The public and the experts
Experts see risk different than the public.
Experts: number of life-years lost versus public who make more
nuanced distinctions
Such as good and bad deaths, deaths during voluntary exercises
versus murder etc.
Paul Slovic: public perception of risk is better and richer than
the experts!
The public and the experts (2)
Cass Sunstein: NO Slovic is wrong.
We need experts to prevent influence of populist movements.
Any risk policy should be measured in number of life years
saved (more weight to the young) and cost in dollar to economy.
Policy makers and government intervention should be LESS
influenced by public opinion.

14. Availability cascades
Minor event, major coverage, more worries and fear, more
coverage, large-scale government action.
Can you come up with an example of an availability cascade?
Implications
Limitation of the mind to deal with minor risk: either ignored or
given to much weight.
Parent waiting up for child to come home.
Knows there's not much to worry but the horrible news stories
cannot be rejected.
The numerator "horror story" is given attention while the
denominator "Instances actually occurred" is ignored.
This is the "probability neglect" effect.
Availability cases DISTORT priorities in the spending of public
resources!
Terrorism vs other causes of risk/threat
Public or experts?
What do you think?
Ignore public fear and go with the experts or…
…forget about the experts and resolve the issues important to
the public?
Public or experts? (2)

15. Sunstein: experts who are independent from public influence
should have the strongest voice in informing policy making.
Slovic: policies not supported by the public will be rejected.
Not sustainable.
Speaking of availability
“Because of the coincidence of two planes crashing last month,
she now prefers to take the train. That’s silly. The risk hasn’t
really changed; it is an availability bias.”
“She has been watching too many spy movies recently, so she’s
seeing conspiracies everywhere.”
“The CEO has had several successes in a row, so failure doesn’t
come easily to his mind. The availability bias is making him
overconfident.”
“She’s raving about an innovation that has large benefits and no
costs. I suspect the affect heuristic.”
“This is an availability cascade: a non-event that is inflated by
the media and the public until it fills our TV screens and
becomes all anyone is talking about.”
Exercise 5
Base rates
You’ve used a base rate: “How many students of a particular
specialization are there?” which leads to rank.
Base: the proportion of units of a particular category divided by
all the units over all categories.

16. Exercise 6
Representativeness
Used the stereotype of Tom while ignoring base rates.
Also ignoring whether description is accurate and from
trustworthy source.
Same description was offered to another scientist, a statistician
and colleague of the researchers who responded "computer
scientist!“
Same experiment done with 114 graduate psychology students
who are aware of base rates and trustworthiness of information.
Same outcome: use stereotype, ignore base rates and quality of
information.
Representativeness
Explanation:
The question to assess probability using base rates is a difficult
question, instead SUBSTITUTED by answering question about
similarity to the stereotypes which is called representativeness,
which is an easier question.
Serious mistake in probability assessment: ignoring quality of
information and base rates.
This mistake is called the representativeness heuristic.

17. Interesting book "Moneyball" about professional baseball.
Scouts judge future success of players on build or looks. The
lead in this story is Billy Beane, manager of the Oakland A's
and who bravely overruled the suggestions by his scouts and
instead hired based on past statistics of performance. Result:
good players against low costs (players who were rejected by
other teams because of unfitting build or look) and eventually
success; excellent results at low cost.
54
THE SINS OF REPRESENTATIVENESS
You see someone on the tube reading the New
York Times. Which of the following is a better
guess?
She has a PhD
She does not have a college degree.
PhD? not wise, there are many more college dropout or people
who didn't start college on the subway than people holding a
PhD.

18. 55
THE SINS OF REPRESENTATIVENESS (2)
The second sin of representativeness is ignoring the quality of
information.
Tom W's info should have been ignored, particularly when
participants were told the information is not trustworthy.
Your system 1 cannot help but to process the information
because of associative coherence and produce the story.
Unless the information is immediately rejected (e.g. this comes
from a liar), your system 1 will start working with it.
How to solve Tom W.
People who frowned did much better. Who can explain why?
Solution
to Tom W.:
Stay very close to your initial estimates.
Reduce slightly the value of the highly populated fields
(humanities and education, social science)
Increase slightly the value of the sparsely populated fields
(library science, computer science).
You will still not be the way you were without having read Tom
W's description but the idea is to make a solid effort to ignore

19. the information and work with base rates.
Bayesian reasoning
You need to discipline your intuition.
18th century English Minister Thomas Bayes:
How should people change their logic in light of evidence?
Mathematical details are not important for this course but
remember two rules about Bayesian reasoning:
Base rates matter, even if information about a particular case is
offered.
Intuitive beliefs about the accuracy of descriptions is
exaggerated.
WYSIATI and associative coherence stimulate us to believe in
the stories we spin for ourselves.
Author’s note: implementation of these rules comes unnatural
and requires effort. I was shocked when I realised I was never
taught how to implement them.
58

20. Speaking of Representativeness
“The lawn is well trimmed, the receptionist looks competent,
and the furniture is attractive, but this doesn’t mean it is a well-
managed company. I hope the board does not go by
representativeness.”
“This start-up looks as if it could not fail, but the base rate of
success in the industry is extremely low. How do we know this
case is different?”
Exercise 7
Linda: less is more
The critical items in the list:
Does Linda look more like a bank teller?
Or more like a bank teller who is active in the environmentalist
movement?
Everyone agrees that Linda fits the idea of a “environmentalist

21. bank teller” better than she fits the stereotype of bank tellers.
Even when scenarios are listed sequentially.
Logic vs representativeness
Same problem offered to doctoral students in the decision-
science program of the Stanford Graduate School of Business,
all of whom had taken several advanced courses in probability,
statistics, and decision theory.
85% of these respondents also ranked “environmentalist bank
teller” as more likely than “bank teller.”
Logic was again beaten by representativeness.
The word fallacy is used, in general, when people fail to apply a
logical rule that is obviously relevant.
Linda problem is a conjunction fallacy.

22. Amos and I introduced the idea of a conjunction fallacy, which
people commit when they judge a conjunction of two events
(here, bank teller and environmentalist) to be more probable
than one of the events (bank teller) in a direct comparison.
63
Implications
The most coherent stories are not always the most probable, but
they are plausible, and ideas of coherence, plausibility, and
probability are easily confused by the incautious.
Adding detail to scenarios makes them more persuasive, but less
likely to come true.
Mark has hair.
Mark has blond hair
Conclusion: In the absence of a competing intuition, logic
prevails.

23. Implications (2)
The less-is-more pattern is bizarre.
In all these cases, the conjunction seemed plausible (not
probable) which was enough for an endorsement of System 2.
Again: lazy system 2.
Representativeness can hinder the application of an obvious
logical rule.
Speaking of less is more
“They constructed a very complicated scenario and insisted on
calling it highly probable. It is not—it is only a plausible
story.”
“They added a cheap gift to the expensive product, and made
the whole deal less attractive. Less is more in this case.”
“In most situations, a direct comparison makes people more
careful and more logical. But not always. Sometimes intuition
beats logic even when the correct answer stares you in the
face.”

24. Experiment
(Nisbett and Borgida)
Helping Experiment conducted at New York University:
participants in individual booths to speak over the intercom
about personal lives. Talk in turn for about 2 minutes. Only 1
microphone active at one time.
6 participants in every round, 1 is a stooge. Stooge speaks first
and tells with embarrassment that he sometimes has seizures.
Automatically microphone to next speaker.
At one point when it's stooge's turn again he becomes distressed
and says he's having a seizure and asks for help in a disturbing
way. Last words: “I'm gonna dieeee”. Then microphone of next
individual became active and nothing was heard from the dying
individual.
Results of NYU experiment:
only 4 out of the 15 (experiment ran 3 times) participants
responded immediately to help.
6 never came out of booth.
5 only after they heard the stooge choking.

25. Experiment (2)
(Nisbett and Borgida)
What would you do?
Conclusion of experiment: expectation is wrong. Most of us
don't help when we expect others to help instead.
Nisbett and Borgida wanted to know: “Have our students
changed their minds about human nature?”
They showed them interviews of people who were part of the
New York experiment. The interviews were short :their hobbies,
their leisure activities, and their future plans, which were
entirely common.
After the interview students were asked: how quick did that
person come out of his booth to help?
Experiment (3)
(Nisbett and Borgida)