Case Study about EthicalA woman was diagnosed with motor neuron.docx

Case Study about Ethical:
A woman was diagnosed with motor neuron disease 5 years ago.
This is a condition that destroys motor nerves, making control
of movement impossible, while the mind is virtually unaffected.
People with motor neuron disease normally die within 4 years of
diagnosis from suffocation due to the inability of the inspiratory
muscles to contract. The woman's condition has steadily
declined. She is not expected to live through the month and is
worried about the pain that she will face in her final hours. She
asks her doctor to give her diamorphine for pain if she begins to
suffocate or choke. This will lessen her pain, but it will also
hasten her death. About a week later, she falls very ill, and is
having trouble breathing.
Questions:
*Does she have a right to make this choice, especially in view
of the fact that she will be dead in a short while (say six hours)?
Please explain
*Is this choice an extension of her autonomy? Please explain
*Is the short amount of time she has to live ethically relevant?
Please explain
*Is there an ethical difference between her dying in 6 hours and
dying in a week? Please explain
*Is the right for a patient's self-determination powerful enough
to create obligations on the part of others to aid her so that she
can exercise her rights? Please explain
* She clearly cannot kill herself. She can't move, but should
someone be FORCED to help her, or to find someone to help
her? Please explain
*Should the money used to care for this woman be taken into
account when she is being helped? Please explain
*Do you think that legalizing euthanasia could create conflicts
of interest for the patient/ or the doctor? Please explain
*Will people feel that they need to end their lives earlier to save
money? Please explain

naked statistics
Stripping the Dread from the Data
CHARLES WHEELAN
Dedication
For Katrina
Contents
Cover
Title Page
Dedication
Introduction: Why I hated calculus but love statistics
1 What’s the Point?
2 Descriptive Statistics: Who was the best baseball player of all
time?
Appendix to Chapter 2

3 Deceptive Description: “He’s got a great personality!” and
other true but
grossly misleading statements
4 Correlation: How does Netflix know what movies I like?
5 Basic Probability: Don’t buy the extended warranty on your
$99 printer
5½ The Monty Hall Problem
6 Problems with Probability: How overconfident math geeks
nearly
destroyed the global financial system
7 The Importance of Data: “Garbage in, garbage out”
8 The Central Limit Theorem: The Lebron James of statistics
9 Inference: Why my statistics professor thought I might have
cheated
kindle:embed:0003?mime=image/jpg
10 Polling: How we know that 64 percent of Americans support
the death
penalty (with a sampling error ± 3 percent)
11 Regression Analysis: The miracle elixir

12 Common Regression Mistakes: The mandatory warning label
13 Program Evaluation: Will going to Harvard change your life?
Conclusion: Five questions that statistics can help answer
Appendix: Statistical software
Notes
Acknowledgments
Index
More praise for Naked Statistics
Also by Charles Wheelan
Copyright
Introduction
Why I hated calculus but love statistics
I have always had an uncomfortable relationship with math. I
don’t like
numbers for the sake of numbers. I am not impressed by fancy
formulas
that have no real-world application. I particularly disliked high
school
calculus for the simple reason that no one ever bothered to tell
me why I
needed to learn it. What is the area beneath a parabola? Who
cares?
In fact, one of the great moments of my life occurred during my
senior

year of high school, at the end of the first semester of Advanced
Placement
Calculus. I was working away on the final exam, admittedly less
prepared
for the exam than I ought to have been. (I had been accepted to
my first-
choice college a few weeks earlier, which had drained away
what little
motivation I had for the course.) As I stared at the final exam
questions,
they looked completely unfamiliar. I don’t mean that I was
having trouble
answering the questions. I mean that I didn’t even recognize
what was
being asked. I was no stranger to being unprepared for exams,
but, to
paraphrase Donald Rumsfeld, I usually knew what I didn’t
know. This
exam looked even more Greek than usual. I flipped through the
pages of the
exam for a while and then more or less surrendered. I walked to
the front of
the classroom, where my calculus teacher, whom we’ll call
Carol Smith,
was proctoring the exam. “Mrs. Smith,” I said, “I don’t
recognize a lot of
the stuff on the test.”
Suffice it to say that Mrs. Smith did not like me a whole lot
more than I
liked her. Yes, I can now admit that I sometimes used my
limited powers as
student association president to schedule all-school assemblies
just so that
Mrs. Smith’s calculus class would be canceled. Yes, my friends
and I did

have flowers delivered to Mrs. Smith during class from “a
secret admirer”
just so that we could chortle away in the back of the room as
she looked
around in embarrassment. And yes, I did stop doing any
homework at all
once I got in to college.
So when I walked up to Mrs. Smith in the middle of the exam
and said
that the material did not look familiar, she was, well,
unsympathetic.
“Charles,” she said loudly, ostensibly to me but facing the rows
of desks to
make certain that the whole class could hear, “if you had
studied, the
material would look a lot more familiar.” This was a compelling
point.
So I slunk back to my desk. After a few minutes, Brian
Arbetter, a far
better calculus student than I, walked to the front of the room
and
whispered a few things to Mrs. Smith. She whispered back and
then a truly
extraordinary thing happened. “Class, I need your attention,”
Mrs. Smith
announced. “It appears that I have given you the second
semester exam by
mistake.” We were far enough into the test period that the
whole exam had
to be aborted and rescheduled.

I cannot fully describe my euphoria. I would go on in life to
marry a
wonderful woman. We have three healthy children. I’ve
published books
and visited places like the Taj Mahal and Angkor Wat. Still, the
day that my
calculus teacher got her comeuppance is a top five life moment.
(The fact
that I nearly failed the makeup final exam did not significantly
diminish this
wonderful life experience.)
The calculus exam incident tells you much of what you need to
know
about my relationship with mathematics—but not everything.
Curiously, I
loved physics in high school, even though physics relies very
heavily on the
very same calculus that I refused to do in Mrs. Smith’s class.
Why? Because
physics has a clear purpose. I distinctly remember my high
school physics
teacher showing us during the World Series how we could use
the basic
formula for acceleration to estimate how far a home run had
been hit. That’s
cool—and the same formula has many more socially significant
applications.
Once I arrived in college, I thoroughly enjoyed probability,
again because
it offered insight into interesting real-life situations. In
hindsight, I now
recognize that it wasn’t the math that bothered me in calculus
class; it was
that no one ever saw fit to explain the point of it. If you’re not

fascinated by
the elegance of formulas alone—which I am most emphatically
not—then it
is just a lot of tedious and mechanistic formulas, at least the
way it was
taught to me.
That brings me to statistics (which, for the purposes of this
book,
includes probability). I love statistics. Statistics can be used to
explain
everything from DNA testing to the idiocy of playing the
lottery. Statistics
can help us identify the factors associated with diseases like
cancer and
heart disease; it can help us spot cheating on standardized tests.
Statistics
can even help you win on game shows. There was a famous
program during
my childhood called Let’s Make a Deal, with its equally famous
host,
Monty Hall. At the end of each day’s show, a successful player
would stand
with Monty facing three big doors: Door no. 1, Door no. 2, and
Door no. 3.
Monty Hall explained to the player that there was a highly
desirable prize
behind one of the doors—something like a new car—and a goat
behind the
other two. The idea was straightforward: the player chose one of
the doors
and would get the contents behind that door.
As each player stood facing the doors with Monty Hall, he or

she had a 1
in 3 chance of choosing the door that would be opened to reveal
the
valuable prize. But Let’s Make a Deal had a twist, which has
delighted
statisticians ever since (and perplexed everyone else). After the
player
chose a door, Monty Hall would open one of the two remaining
doors,
always revealing a goat. For the sake of example, assume that
the player has
chosen Door no. 1. Monty would then open Door no. 3; the live
goat would
be standing there on stage. Two doors would still be closed,
nos. 1 and 2. If
the valuable prize was behind no. 1, the contestant would win;
if it was
behind no. 2, he would lose. But then things got more
interesting: Monty
would turn to the player and ask whether he would like to
change his mind
and switch doors (from no. 1 to no. 2 in this case). Remember,
both doors
were still closed, and the only new information the contestant
had received
was that a goat showed up behind one of the doors that he didn’t
pick.
Should he switch?
The answer is yes. Why? That’s in Chapter 5½.
The paradox of statistics is that they are everywhere—from
batting
averages to presidential polls—but the discipline itself has a
reputation for
being uninteresting and inaccessible. Many statistics books and

classes are
overly laden with math and jargon. Believe me, the technical
details are
crucial (and interesting)—but it’s just Greek if you don’t
understand the
intuition. And you may not even care about the intuition if
you’re not
convinced that there is any reason to learn it. Every chapter in
this book
promises to answer the basic question that I asked (to no effect)
of my high
school calculus teacher: What is the point of this?
This book is about the intuition. It is short on math, equations,
and
graphs; when they are used, I promise that they will have a clear
and
enlightening purpose. Meanwhile, the book is long on examples
to convince
you that there are great reasons to learn this stuff. Statistics can
be really
interesting, and most of it isn’t that difficult.
The idea for this book was born not terribly long after my
unfortunate
experience in Mrs. Smith’s AP Calculus class. I went to
graduate school to
study economics and public policy. Before the program even
started, I was
assigned (not surprisingly) to “math camp” along with the bulk
of my
classmates to prepare us for the quantitative rigors that were to
follow. For

three weeks, we learned math all day in a windowless, basement
classroom
(really).
On one of those days, I had something very close to a career
epiphany.
Our instructor was trying to teach us the circumstances under
which the
sum of an infinite series converges to a finite number. Stay with
me here for
a minute because this concept will become clear. (Right now
you’re
probably feeling the way I did in that windowless classroom.)
An infinite
series is a pattern of numbers that goes on forever, such as 1 +
½ + ¼ + ⅛ .
. . The three dots means that the pattern continues to infinity.
This is the part we were having trouble wrapping our heads
around. Our
instructor was trying to convince us, using some proof I’ve long
since
forgotten, that a series of numbers can go on forever and yet
still add up
(roughly) to a finite number. One of my classmates, Will
Warshauer, would
have none of it, despite the impressive mathematical proof. (To
be honest, I
was a bit skeptical myself.) How can something that is infinite
add up to
something that is finite?
Then I got an inspiration, or more accurately, the intuition of
what the
instructor was trying to explain. I turned to Will and talked him
through

what I had just worked out in my head. Imagine that you have
positioned
yourself exactly 2 feet from a wall.
Now move half the distance to that wall (1 foot), so that you are
left
standing 1 foot away.
From 1 foot away, move half the distance to the wall once again
(6
inches, or ½ a foot). And from 6 inches away, do it again (move
3 inches,
or ¼ of a foot). Then do it again (move 1½ inches, or ⅛ of a
foot). And so
on.
You will gradually get pretty darn close to the wall. (For
example, when
you are 1/1024th of an inch from the wall, you will move half
the distance,
or another 1/2048th of an inch.) But you will never hit the wall,
because by
definition each move takes you only half the remaining
distance. In other
words, you will get infinitely close to the wall but never hit it.
If we
measure your moves in feet, the series can be described as 1 +
½ + ¼ + ⅛ .
. .
Therein lies the insight: Even though you will continue moving
forever
—with each move taking you half the remaining distance to the
wall—the

total distance you travel can never be more than 2 feet, which is
your
starting distance from the wall. For mathematical purposes, the
total
distance you travel can be approximated as 2 feet, which turns
out to be
very handy for computation purposes. A mathematician would
say that the
sum of this infinite series 1 ft + ½ ft + ¼ ft + ⅛ ft . . .
converges to 2 feet,
which is what our instructor was trying to teach us that day.
The point is that I convinced Will. I convinced myself. I can’t
remember
the math proving that the sum of an infinite series can converge
to a finite
number, but I can always look that up online. And when I do, it
will
probably make sense. In my experience, the intuition makes the
math and
other technical details more understandable—but not necessarily
the other
way around.
The point of this book is to make the most important statistical
concepts
more intuitive and more accessible, not just for those of us
forced to study
them in windowless classrooms but for anyone interested in the
extraordinary power of numbers and data.
Now, having just made the case that the core tools of statistics
are less
intuitive and accessible than they ought to be, I’m going to
make a
seemingly contradictory point: Statistics can be overly

accessible in the
sense that anyone with data and a computer can do sophisticated
statistical
procedures with a few keystrokes. The problem is that if the
data are poor,
or if the statistical techniques are used improperly, the
conclusions can be
wildly misleading and even potentially dangerous. Consider the
following
hypothetical Internet news flash: People Who Take Short Breaks
at Work
Are Far More Likely to Die of Cancer. Imagine that headline
popping up
while you are surfing the Web. According to a seemingly
impressive study
of 36,000 office workers (a huge data set!), those workers who
reported
leaving their offices to take regular ten-minute breaks during
the workday
were 41 percent more likely to develop cancer over the next five
years than
workers who don’t leave their offices during the workday.
Clearly we need
to act on this kind of finding—perhaps some kind of national
awareness
campaign to prevent short breaks on the job.
Or maybe we just need to think more clearly about what many
workers
are doing during that ten-minute break. My professional
experience
suggests that many of those workers who report leaving their
offices for

short breaks are huddled outside the entrance of the building
smoking
cigarettes (creating a haze of smoke through which the rest of
us have to
walk in order to get in or out). I would further infer that it’s
probably the
cigarettes, and not the short breaks from work, that are causing
the cancer.
I’ve made up this example just so that it would be particularly
absurd, but I
can assure you that many real-life statistical abominations are
nearly this
absurd once they are deconstructed.
Statistics is like a high-caliber weapon: helpful when used
correctly and
potentially disastrous in the wrong hands. This book will not
make you a
statistical expert; it will teach you enough care and respect for
the field that
you don’t do the statistical equivalent of blowing someone’s
head off.
This is not a textbook, which is liberating in terms of the topics
that have
to be covered and the ways in which they can be explained. The
book has
been designed to introduce the statistical concepts with the most
relevance
to everyday life. How do scientists conclude that something
causes cancer?
How does polling work (and what can go wrong)? Who “lies
with
statistics,” and how do they do it? How does your credit card
company use
data on what you are buying to predict if you are likely to miss

a payment?
(Seriously, they can do that.)
If you want to understand the numbers behind the news and to
appreciate
the extraordinary (and growing) power of data, this is the stuff
you need to
know. In the end, I hope to persuade you of the observation first
made by
Swedish mathematician and writer Andrejs Dunkels: It’s easy to
lie with
statistics, but it’s hard to tell the truth without them.
But I have even bolder aspirations than that. I think you might
actually
enjoy statistics. The underlying ideas are fabulously interesting
and
relevant. The key is to separate the important ideas from the
arcane
technical details that can get in the way. That is Naked
Statistics.
CHAPTER 1
What’s the Point?
I’ve noticed a curious phenomenon. Students will complain that
statistics is
confusing and irrelevant. Then the same students will leave the
classroom
and happily talk over lunch about batting averages (during the

summer) or
the windchill factor (during the winter) or grade point averages
(always).
They will recognize that the National Football League’s “passer
rating”—a
statistic that condenses a quarterback’s performance into a
single number—
is a somewhat flawed and arbitrary measure of a quarterback’s
game day
performance. The same data (completion rate, average yards per
pass
attempt, percentage of touchdown passes per pass attempt, and
interception
rate) could be combined in a different way, such as giving
greater or lesser
weight to any of those inputs, to generate a different but equally
credible
measure of performance. Yet anyone who has watched football
recognizes
that it’s handy to have a single number that can be used to
encapsulate a
quarterback’s performance.
Is the quarterback rating perfect? No. Statistics rarely offers a
single
“right” way of doing anything. Does it provide meaningful
information in
an easily accessible way? Absolutely. It’s a nice tool for making
a quick
comparison between the performances of two quarterbacks on a
given day. I
am a Chicago Bears fan. During the 2011 playoffs, the Bears
played the
Packers; the Packers won. There are a lot of ways I could
describe that
game, including pages and pages of analysis and raw data. But

here is a
more succinct analysis. Chicago Bears quarterback Jay Cutler
had a passer
rating of 31.8. In contrast, Green Bay quarterback Aaron
Rodgers had a
passer rating of 55.4. Similarly, we can compare Jay Cutler’s
performance
to that in a game earlier in the season against Green Bay, when
he had a
passer rating of 85.6. That tells you a lot of what you need to
know in order
to understand why the Bears beat the Packers earlier in the
season but lost
to them in the playoffs.
That is a very helpful synopsis of what happened on the field.
Does it
simplify things? Yes, that is both the strength and the weakness
of any
descriptive statistic. One number tells you that Jay Cutler was
outgunned by
Aaron Rodgers in the Bears’ playoff loss. On the other hand,
that number
won’t tell you whether a quarterback had a bad break, such as
throwing a
perfect pass that was bobbled by the receiver and then
intercepted, or
whether he “stepped up” on certain key plays (since every
completion is
weighted the same, whether it is a crucial third down or a
meaningless play
at the end of the game), or whether the defense was terrible.
And so on.

The curious thing is that the same people who are perfectly
comfortable
discussing statistics in the context of sports or the weather or
grades will
seize up with anxiety when a researcher starts to explain
something like the
Gini index, which is a standard tool in economics for measuring
income
inequality. I’ll explain what the Gini index is in a moment, but
for now the
most important thing to recognize is that the Gini index is just
like the
passer rating. It’s a handy tool for collapsing complex
information into a
single number. As such, it has the strengths of most descriptive
statistics,
namely that it provides an easy way to compare the income
distribution in
two countries, or in a single country at different points in time.
The Gini index measures how evenly wealth (or income) is
shared within
a country on a scale from zero to one. The statistic can be
calculated for
wealth or for annual income, and it can be calculated at the
individual level
or at the household level. (All of these statistics will be highly
correlated
but not identical.) The Gini index, like the passer rating, has no
intrinsic
meaning; it’s a tool for comparison. A country in which every
household
had identical wealth would have a Gini index of zero. By
contrast, a country
in which a single household held the country’s entire wealth
would have a

Gini index of one. As you can probably surmise, the closer a
country is to
one, the more unequal its distribution of wealth. The United
States has a
Gini index of .45, according to the Central Intelligence Agency
(a great
collector of statistics, by the way).1 So what?
Once that number is put into context, it can tell us a lot. For
example,
Sweden has a Gini index of .23. Canada’s is .32. China’s is .42.
Brazil’s is
.54. South Africa’s is .65.* As we look across those numbers,
we get a sense
of where the United States falls relative to the rest of the world
when it
comes to income inequality. We can also compare different
points in time.
The Gini index for the United States was .41 in 1997 and grew
to .45 over
the next decade. (The most recent CIA data are for 2007.) This
tells us in an
objective way that while the United States grew richer over that
period of
time, the distribution of wealth grew more unequal. Again, we
can compare
the changes in the Gini index across countries over roughly the
same time
period. Inequality in Canada was basically unchanged over the
same
stretch. Sweden has had significant economic growth over the
past two
decades, but the Gini index in Sweden actually fell from .25 in

1992 to .23
in 2005, meaning that Sweden grew richer and more equal over
that period.
Is the Gini index the perfect measure of inequality? Absolutely
not—just
as the passer rating is not a perfect measure of quarterback
performance.
But it certainly gives us some valuable information on a
socially significant
phenomenon in a convenient format.
We have also slowly backed our way into answering the
question posed
in the chapter title: What is the point? The point is that
statistics helps us
process data, which is really just a fancy name for information.
Sometimes
the data are trivial in the grand scheme of things, as with sports
statistics.
Sometimes they offer insight into the nature of human
existence, as with the
Gini index.
But, as any good infomercial would point out, That’s not all!
Hal Varian,
chief economist at Google, told the New York Times that being
a statistician
will be “the sexy job” over the next decade.2 I’ll be the first to
concede that
economists sometimes have a warped definition of “sexy.” Still,
consider
the following disparate questions:
How can we catch schools that are cheating on their
standardized tests?

How does Netflix know what kind of movies you like?
How can we figure out what substances or behaviors cause
cancer, given
that we cannot conduct cancer-causing experiments on humans?
Does praying for surgical patients improve their outcomes?
Is there really an economic benefit to getting a degree from a
highly
selective college or university?
What is causing the rising incidence of autism?
Statistics can help answer these questions (or, we hope, can
soon). The
world is producing more and more data, ever faster and faster.
Yet, as the
New York Times has noted, “Data is merely the raw material of
knowledge.”3* Statistics is the most powerful tool we have for
using
information to some meaningful end, whether that is identifying
underrated
baseball players or paying teachers more fairly. Here is a quick
tour of how
statistics can bring meaning to raw data.
Description and Comparison
A bowling score is a descriptive statistic. So is a batting
average. Most
American sports fans over the age of five are already conversant
in the field
of descriptive statistics. We use numbers, in sports and
everywhere else in

life, to summarize information. How good a baseball player was
Mickey
Mantle? He was a career .298 hitter. To a baseball fan, that is a
meaningful
statement, which is remarkable when you think about it, because
it
encapsulates an eighteen-season career.4 (There is, I suppose,
something
mildly depressing about having one’s lifework collapsed into a
single
number.) Of course, baseball fans have also come to recognize
that
descriptive statistics other than batting average may better
encapsulate a
player’s value on the field.
We evaluate the academic performance of high school and
college
students by means of a grade point average, or GPA. A letter
grade is
assigned a point value; typically an A is worth 4 points, a B is
worth 3, a C
is worth 2, and so on. By graduation, when high school students
are
applying to college and college students are looking for jobs,
the grade
point average is a handy tool for assessing their academic
potential.
Someone who has a 3.7 GPA is clearly a stronger student than
someone at
the same school with a 2.5 GPA. That makes it a nice
descriptive statistic.
It’s easy to calculate, it’s easy to understand, and it’s easy to
compare across
students.

But it’s not perfect. The GPA does not reflect the difficulty of
the courses
that different students may have taken. How can we compare a
student with
a 3.4 GPA in classes that appear to be relatively nonchallenging
and a
student with a 2.9 GPA who has taken calculus, physics, and
other tough
subjects? I went to a high school that attempted to solve this
problem by
giving extra weight to difficult classes, so that an A in an
“honors” class
was worth five points instead of the usual four. This caused its
own
problems. My mother was quick to recognize the distortion
caused by this
GPA “fix.” For a student taking a lot of honors classes (me),
any A in a
nonhonors course, such as gym or health education, would
actually pull my
GPA down, even though it is impossible to do better than an A
in those
classes. As a result, my parents forbade me to take driver’s
education in
high school, lest even a perfect performance diminish my
chances of getting
into a competitive college and going on to write popular books.
Instead,
they paid to send me to a private driving school, at nights over
the summer.
Was that insane? Yes. But one theme of this book will be that
an

overreliance on any descriptive statistic can lead to misleading
conclusions,
or cause undesirable behavior. My original draft of that
sentence used the
phrase “oversimplified descriptive statistic,” but I struck the
word
“oversimplified” because it’s redundant. Descriptive statistics
exist to
simplify, which always implies some loss of nuance or detail.
Anyone
working with numbers needs to recognize as much.
Inference
How many homeless people live on the streets of Chicago? How
often do
married people have sex? These may seem like wildly different
kinds of
questions; in fact, they both can be answered (not perfectly) by
the use of
basic statistical tools. One key function of statistics is to use
the data we
have to make informed conjectures about larger questions for
which we do
not have full information. In short, we can use data from the
“known world”
to make informed inferences about the “unknown world.”
Let’s begin with the homeless question. It is expensive and
logistically
difficult to count the homeless population in a large
metropolitan area. Yet it
is important to have a numerical estimate of this population for
purposes of
providing social services, earning eligibility for state and
federal revenues,

and gaining congressional representation. One important
statistical practice
is sampling, which is the process of gathering data for a small
area, say, a
handful of census tracts, and then using those data to make an
informed
judgment, or inference, about the homeless population for the
city as a
whole. Sampling requires far less resources than trying to count
an entire
population; done properly, it can be every bit as accurate.
A political poll is one form of sampling. A research
organization will
attempt to contact a sample of households that are broadly
representative of
the larger population and ask them their views about a
particular issue or
candidate. This is obviously much cheaper and faster than
trying to contact
every household in an entire state or country. The polling and
research firm
Gallup reckons that a methodologically sound poll of 1,000
households will
produce roughly the same results as a poll that attempted to
contact every
household in America.
That’s how we figured out how often Americans are having sex,
with
whom, and what kind. In the mid-1990s, the National Opinion
Research
Center at the University of Chicago carried out a remarkably

ambitious
study of American sexual behavior. The results were based on
detailed
surveys conducted in person with a large, representative sample
of
American adults. If you read on, Chapter 10 will tell you what
they learned.
How many other statistics books can promise you that?
Assessing Risk and Other Probability-Related Events
Casinos make money in the long run—always. That does not
mean that they
are making money at any given moment. When the bells and
whistles go
off, some high roller has just won thousands of dollars. The
whole gambling
industry is built on games of chance, meaning that the outcome
of any
particular roll of the dice or turn of the card is uncertain. At the
same time,
the underlying probabilities for the relevant events—drawing 21
at
blackjack or spinning red in roulette—are known. When the
underlying
probabilities favor the casinos (as they always …
Predictably irrational
revised

and expanded
edition
The Hidden Forces
That Shape Our Decisions
Dan Ariely
Dedication
To my mentors, colleagues, and students—
who make research exciting
Contents
DEDICATION
INTRODUCTION
How an Injury Led Me to Irrationality and to the
Research Described Here
CHAPTER 1 - The Truth about Relativity
Why Everything Is Relative—Even When It Shouldn’t Be
CHAPTER 2 - The Fallacy of Supply and Demand
Why the Price of Pearls—and Everything Else—Is Up in the Air
CHAPTER 3 - The Cost of Zero Cost
Why We Often Pay Too Much When We Pay Nothing

CHAPTER 4 - The Cost of Social Norms
Why We Are Happy to Do Things, but Not When We Are Paid
to Do
Them
CHAPTER 5 - The Power of a Free Cookie
CHAPTER 6 - The Influence of Arousal
Why Hot Is Much Hotter Than We Realize
CHAPTER 7 - The Problem of Procrastination and Self-Control
Why We Can’t Make Ourselves Do What We Want to Do
CHAPTER 8 - The High Price of Ownership
Why We Overvalue What We Have
CHAPTER 9 - Keeping Doors Open
Why Options Distract Us from Our Main Objective
CHAPTER 10 - The Effect of Expectations
Why the Mind Gets What It Expects
CHAPTER 11 - The Power of Price
Why a 50-Cent Aspirin Can Do What a Penny Aspirin Can’t
CHAPTER 12 - The Cycle of Distrust
CHAPTER 13 - The Context of Our Character, Part I
Why We Are Dishonest, and What We Can Do about It
CHAPTER 14 - The Context of Our Character, Part II
Why Dealing with Cash Makes Us More Honest

CHAPTER 15 - Beer and Free Lunches
What Is Behavioral Economics, and Where Are the Free
Lunches?
THANKS
LIST OF COLLABORATORS
NOTES
BIBLIOGRAPHY AND ADDITIONAL READINGS
ABOUT THE AUTHOR
PRAISE FOR PREDICTABLY IRRATIONAL
COPYRIGHT
ABOUT THE PUBLISHER
Introduction
How an Injury Led Me to Irrationality and
to the Research Described Here
I have been told by many people that I have an unusual way of
looking at
the world. Over the last 20 years or so of my research career,
it’s enabled
me to have a lot of fun figuring out what really influences our
decisions in
daily life (as opposed to what we think, often with great
confidence,

influences them).
Do you know why we so often promise ourselves to diet, only to
have
the thought vanish when the dessert cart rolls by?
Do you know why we sometimes find ourselves excitedly
buying
things we don’t really need?
Do you know why we still have a headache after taking a one-
cent
aspirin, but why that same headache vanishes when the aspirin
costs 50
cents?
Do you know why people who have been asked to recall the Ten
Commandments tend to be more honest (at least immediately
afterward)
than those who haven’t? Or why honor codes actually do reduce
dishonesty
in the workplace?
By the end of this book, you’ll know the answers to these and
many
other questions that have implications for your personal life, for
your
business life, and for the way you look at the world.
Understanding the
answer to the question about aspirin, for example, has
implications not only
for your choice of drugs, but for one of the biggest issues facing
our
society: the cost and effectiveness of health insurance.
Understanding the
impact of the Ten Commandments in curbing dishonesty might

help prevent
the next Enron-like fraud. And understanding the dynamics of
impulsive
eating has implications for every other impulsive decision in
our lives—
including why it’s so hard to save money for a rainy day.
My goal, by the end of this book, is to help you fundamentally
rethink what makes you and the people around you tick. I hope
to lead you
there by presenting a wide range of scientific experiments,
findings, and
anecdotes that are in many cases quite amusing. Once you see
how
systematic certain mistakes are—how we repeat them again and
again—I
think you will begin to learn how to avoid some of them.
But before I tell you about my curious, practical, entertaining
(and in
some cases even delicious) research on eating, shopping, love,
money,
procrastination, beer, honesty, and other areas of life, I feel it is
important
that I tell you about the origins of my somewhat unorthodox
worldview—
and therefore of this book. Tragically, my introduction to this
arena started
with an accident many years ago that was anything but amusing.
ON WHAT WOULD otherwise have been a normal Friday
afternoon in the
life of an eighteen-year-old Israeli, everything changed

irreversibly in a
matter of a few seconds. An explosion of a large magnesium
flare, the kind
used to illuminate battlefields at night, left 70 percent of my
body covered
with third-degree burns.
The next three years found me wrapped in bandages in a
hospital and
then emerging into public only occasionally, dressed in a tight
synthetic suit
and mask that made me look like a crooked version of Spider-
Man. Without
the ability to participate in the same daily activities as my
friends and
family, I felt partially separated from society and as a
consequence started
to observe the very activities that were once my daily routine as
if I were an
outsider. As if I had come from a different culture (or planet), I
started
reflecting on the goals of different behaviors, mine and those of
others. For
example, I started wondering why I loved one girl but not
another, why my
daily routine was designed to be comfortable for the physicians
but not for
me, why I loved going rock climbing but not studying history,
why I cared
so much about what other people thought of me, and mostly
what it is about
life that motivates people and causes us to behave as we do.
During the years in the hospital following my accident, I had

extensive experience with different types of pain and a great
deal of time
between treatments and operations to reflect on it. Initially, my
daily agony
was largely played out in the “bath,” a procedure in which I was
soaked in
disinfectant solution, the bandages were removed, and the dead
particles of
skin were scraped off. When the skin is intact, disinfectants
create a low-
level sting, and in general the bandages come off easily. But
when there is
little or no skin—as in my case because of my extensive
burns—the
disinfectant stings unbearably, the bandages stick to the flesh,
and removing
them (often tearing them) hurts like nothing else I can describe.
Early on in the burn department I started talking to the nurses
who
administered my daily bath, in order to understand their
approach to my
treatment. The nurses would routinely grab hold of a bandage
and rip it off
as fast as possible, creating a relatively short burst of pain; they
would
repeat this process for an hour or so until they had removed
every one of the
bandages. Once this process was over I was covered with
ointment and with
new bandages, in order to repeat the process again the next day.
The nurses, I quickly learned, had theorized that a vigorous tug
at the
bandages, which caused a sharp spike of pain, was preferable
(to the

patient) to a slow pulling of the wrappings, which might not
lead to such a
severe spike of pain but would extend the treatment, and
therefore be more
painful overall. The nurses had also concluded that there was no
difference
between two possible methods: starting at the most painful part
of the body
and working their way to the least painful part; or starting at the
least
painful part and advancing to the most excruciating areas.
As someone who had actually experienced the pain of the
bandage
removal process, I did not share their beliefs (which had never
been
scientifically tested). Moreover, their theories gave no
consideration to the
amount of fear that the patient felt anticipating the treatment; to
the
difficulties of dealing with fluctuations of pain over time; to the
unpredictability of not knowing when the pain will start and
ease off; or to
the benefits of being comforted with the possibility that the pain
would be
reduced over time. But, given my helpless position, I had little
influence
over the way I was treated.
As soon as I was able to leave the hospital for a prolonged
period (I
would still return for occasional operations and treatments for
another five
years), I began studying at Tel Aviv University. During my first

semester, I
took a class that profoundly changed my outlook on research
and largely
determined my future. This was a class on the physiology of the
brain,
taught by professor Hanan Frenk. In addition to the fascinating
material
Professor Frenk presented about the workings of the brain, what
struck me
most about this class was his attitude to questions and
alternative theories.
Many times, when I raised my hand in class or stopped by his
office to
suggest a different interpretation of some results he had
presented, he
replied that my theory was indeed a possibility (somewhat
unlikely, but a
possibility nevertheless)—and would then challenge me to
propose an
empirical test to distinguish it from the conventional theory.
Coming up with such tests was not easy, but the idea that
science is
an empirical endeavor in which all the participants, including a
new student
like myself, could come up with alternative theories, as long as
they found
empirical ways to test these theories, opened up a new world to
me. On one
of my visits to Professor Frenk’s office, I proposed a theory
explaining how
a certain stage of epilepsy developed, and included an idea for
how one
might test it in rats.
Professor Frenk liked the idea, and for the next three months I

operated on about 50 rats, implanting catheters in their spinal
cords and
giving them different substances to create and reduce their
epileptic
seizures. One of the practical problems with this approach was
that the
movements of my hands were very limited, because of my
injury, and as a
consequence it was very difficult for me to operate on the rats.
Luckily for
me, my best friend, Ron Weisberg (an avid vegetarian and
animal lover),
agreed to come with me to the lab for several weekends and
help me with
the procedures—a true test of friendship if ever there was one.
In the end, it turned out that my theory was wrong, but this did
not
diminish my enthusiasm. I was able to learn something about
my theory,
after all, and even though the theory was wrong, it was good to
know this
with high certainty. I always had many questions about how
things work
and how people behave, and my new understanding—that
science provides
the tools and opportunities to examine anything I found
interesting—lured
me into the study of how people behave.
With these new tools, I focused much of my initial efforts on
understanding how we experience pain. For obvious reasons I
was most

concerned with such situations as the bath treatment, in which
pain must be
delivered to a patient over a long period of time. Was it
possible to reduce
the overall agony of such pain? Over the next few years I was
able to carry
out a set of laboratory experiments on myself, my friends, and
volunteers—
using physical pain induced by heat, cold water, pressure, loud
sounds, and
even the psychological pain of losing money in the stock
market—to probe
for the answers.
By the time I had finished, I realized that the nurses in the burn
unit
were kind and generous individuals (well, there was one
exception) with a
lot of experience in soaking and removing bandages, but they
still didn’t
have the right theory about what would minimize their patients’
pain. How
could they be so wrong, I wondered, considering their vast
experience?
Since I knew these nurses personally, I knew that their behavior
was not
due to maliciousness, stupidity, or neglect. Rather, they were
most likely the
victims of inherent biases in their perceptions of their patients’
pain—biases
that apparently were not altered even by their vast experience.
For these reasons, I was particularly excited when I returned to
the
burn department one morning and presented my results, in the
hope of

influencing the bandage removal procedures for other patients.
It turns out, I
told the nurses and physicians, that people feel less pain if
treatments (such
as removing bandages in a bath) are carried out with lower
intensity and
longer duration than if the same goal is achieved through high
intensity and
a shorter duration. In other words, I would have suffered less if
they had
pulled the bandages off slowly rather than with their quick-pull
method.
The nurses were genuinely surprised by my conclusions, but I
was
equally surprised by what Etty, my favorite nurse, had to say.
She admitted
that their understanding had been lacking and that they should
change their
methods. But she also pointed out that a discussion of the pain
inflicted in
the bath treatment should also take into account the
psychological pain that
the nurses experienced when their patients screamed in agony.
Pulling the
bandages quickly might be more understandable, she explained,
if it were
indeed the nurses’ way of shortening their own torment (and
their faces
often did reveal that they were suffering). In the end, though,
we all agreed
that the procedures should be changed, and indeed, some of the
nurses

followed my recommendations.
My recommendations never changed the bandage removal
process on
a greater scale (as far as I know), but the episode left a special
impression
on me. If the nurses, with all their experience, misunderstood
what
constituted reality for the patients they cared so much about,
perhaps other
people similarly misunderstand the consequences of their
behaviors and, for
that reason, repeatedly make the wrong decisions. I decided to
expand my
scope of research, from pain to the examination of cases in
which
individuals make repeated mistakes—without being able to
learn much
from their experiences.
THIS JOURNEY INTO the many ways in which we are all
irrational, then, is
what this book is about. The discipline that allows me to play
with this
subject matter is called behavioral economics, or judgment and
decision
making (JDM).
Behavioral economics is a relatively new field, one that draws
on aspects
of both psychology and economics. It has led me to study
everything from
our reluctance to save for retirement to our inability to think
clearly during
sexual arousal. It’s not just the behavior that I have tried to
understand,

though, but also the decision-making processes behind such
behavior—
yours, mine, and everybody else’s. Before I go on, let me try to
explain,
briefly, what behavioral economics is all about and how it is
different from
standard economics. Let me start out with a bit of Shakespeare:
What a piece of work is a man! how noble in reason! how
infinite in
faculty! in form and moving how express and admirable! in
action
how like an angel! in apprehension how like a god! The beauty
of
the world, the paragon of animals. —from Act II, scene 2, of
Hamlet
The predominant view of human nature, largely shared by
economists,
policy makers, nonprofessionals, and everyday Joes, is the one
reflected in
this quotation. Of course, this view is largely correct. Our
minds and bodies
are capable of amazing acts. We can see a ball thrown from a
distance,
instantly calculate its trajectory and impact, and then move our
body and
hands in order to catch it. We can learn new languages with
ease,
particularly as young children. We can master chess. We can
recognize
thousands of faces without confusing them. We can produce
music,

literature, technology, and art—and the list goes on and on.
Shakespeare is not alone in his appreciation for the human
mind. In
fact, we all think of ourselves along the lines of Shakespeare’s
depiction
(although we do realize that our neighbors, spouses, and bosses
do not
always live up to this standard). Within the domain of science,
these
assumptions about our ability for perfect reasoning have found
their way
into economics. In economics, this very basic idea, called
rationality,
provides the foundation for economic theories, predictions, and
recommendations.
From this perspective, and to the extent that we all believe in
human
rationality, we are all economists. I don’t mean that each of us
can
intuitively develop complex game-theoretical models or
understand the
generalized axiom of revealed preference (GARP); rather, I
mean that we
hold the basic beliefs about human nature on which economics
is built. In
this book, when I mention the rational economic model, I refer
to the basic
assumption that most economists and many of us hold about
human nature
—the simple and compelling idea that we are capable of making
the right
decisions for ourselves.
Although a feeling of awe at the capability of humans is clearly

justified, there is a large difference between a deep sense of
admiration and
the assumption that our reasoning abilities are perfect. In fact,
this book is
about human irrationality—about our distance from perfection. I
believe
that recognizing where we depart from the ideal is an important
part of the
quest to truly understand ourselves, and one that promises many
practical
benefits. Understanding irrationality is important for our
everyday actions
and decisions, and for understanding how we design our
environment and
the choices it presents to us.
My further observation is that we are not only irrational, but
predictably irrational—that our irrationality happens the same
way, again
and again. Whether we are acting as consumers, businesspeople,
or policy
makers, understanding how we are predictably irrational
provides a starting
point for improving our decision making and changing the way
we live for
the better.
This leads me to the real “rub” (as Shakespeare might have
called it)
between conventional economics and behavioral economics. In
conventional economics, the assumption that we are all rational
implies
that, in everyday life, we compute the value of all the options

we face and
then follow the best possible path of action. What if we make a
mistake and
do something irrational? Here, too, traditional economics has an
answer:
“market forces” will sweep down on us and swiftly set us back
on the path
of righteousness and rationality. On the basis of these
assumptions, in fact,
generations of economists since Adam Smith have been able to
develop far-
reaching conclusions about everything from taxation and health-
care
policies to the pricing of goods and services.
But, as you will see in this book, we are really far less rational
than
standard economic theory assumes. Moreover, these irrational
behaviors of
ours are neither random nor senseless. They are systematic, and
since we
repeat them again and again, predictable. So, wouldn’t it make
sense to
modify standard economics, to move it away from naive
psychology (which
often fails the tests of reason, introspection, and—most
important—
empirical scrutiny)? This is exactly what the emerging field of
behavioral
economics, and this book as a small part of that enterprise, is
trying to
accomplish.
AS YOU WILL see in the pages ahead, each of the chapters in
this book is
based on a few experiments I carried out over the years with

some terrific
colleagues (at the end of the book, I have included short
biographies of my
amazing collaborators). Why experiments? Life is complex,
with multiple
forces simultaneously exerting their influences on us, and this
complexity
makes it difficult to figure out exactly how each of these forces
shapes our
behavior. For social scientists, experiments are like microscopes
or strobe
lights. They help us slow human behavior to a frame-by-frame
narration of
events, isolate individual forces, and examine those forces
carefully and in
more detail. They let us test directly and unambiguously what
makes us
tick.
There is one other point I want to emphasize about experiments.
If
the lessons learned in any experiment were limited to the exact
environment
of the experiment, their value would be limited. Instead, I
would like you to
think about experiments as an illustration of a general principle,
providing
insight into how we think and how we make decisions—not only
in the
context of a particular experiment but, by extrapolation, in
many contexts of
life.
In each chapter, then, I have taken a step in extrapolating the

findings
from the experiments to other contexts, attempting to describe
some of their
possible implications for life, business, and public policy. The
implications
I have drawn are, of course, just a partial list.
To get real value from this, and from social science in general,
it is
important that you, the reader, spend some time thinking about
how the
principles of human behavior identified in the experiments
apply to your
life. My suggestion to you is to pause at the end of each chapter
and
consider whether the principles revealed in the experiments
might make
your life better or worse, and more importantly what you could
do
differently, given your new understanding of human nature.
This is where
the real adventure lies.
And now for the journey.
CHAPTER 1
The Truth about Relativity
Why Everything Is Relative—Even
When It Shouldn’t Be
One day while browsing the World Wide Web (obviously for

work—not
just wasting time), I stumbled on the following ad, on the Web
site of a
magazine, the Economist.
I read these offers one at a time. The first offer—the Internet
subscription for $59—seemed reasonable. The second option—
the $125
print subscription—seemed a bit expensive, but still reasonable.
But then I read the third option: a print and Internet
subscription for
$125. I read it twice before my eye ran back to the previous
options. Who
would want to buy the print option alone, I wondered, when
both the
Internet and the print subscriptions were offered for the same
price? Now,
the print-only option may have been a typographical error, but I
suspect that
the clever people at the Economist’s London offices (and they
are clever—
and quite mischievous in a British sort of way) were actually
manipulating
me. I am pretty certain that they wanted me to skip the Internet-
only option
(which they assumed would be my choice, since I was reading
the
advertisement on the Web) and jump to the more expensive
option: Internet
and print.

But how could they manipulate me? I suspect it’s because the
Economist’s marketing wizards (and I could just picture them in
their school
ties and blazers) knew something important about human
behavior: humans
rarely choose things in absolute terms. We don’t have an
internal value
meter that tells us how much things are worth. Rather, we focus
on the
relative advantage of one thing over another, and estimate value
accordingly.
(For instance, we don’t know how much a six-cylinder car is
worth, but we
can assume it’s more expensive than the four-cylinder model.)
In the case of the Economist, I may not have known whether the
Internet-only subscription at $59 was a better deal than the
print-only option
at $125. But I certainly knew that the print-and-Internet option
for $125 was
better than the print-only option at $125. In fact, you could
reasonably
deduce that in the combination package, the Internet
subscription is free!
“It’s a bloody steal—go for it, governor!” I could almost hear
them shout
from the riverbanks of the Thames. And I have to admit, if I had
been
inclined to subscribe I probably would have taken the package
deal myself.
(Later, when I tested the offer on a large number of participants,
the vast
majority preferred the Internet-and-print deal.)
So what was going on here? Let me start with a fundamental

observation: most people don’t know what they want unless
they see it in
context. We don’t know what kind of racing bike we want—
until we see a
champ in the Tour de France ratcheting the gears on a particular
model. We
don’t know what kind of speaker system we like—until we hear
a set of
speakers that sounds better than the previous one. We don’t
even know what
we want to do with our lives—until we find a relative or a
friend who is
doing just what we think we should be doing. Everything is
relative, and
that’s the point. Like an airplane pilot landing in the dark, we
want runway
lights on either side of us, guiding us to the place where we can
touch down
our wheels.
In the case of the Economist, the decision between the Internet-
only
and print-only options would take a bit of thinking. Thinking is
difficult and
sometimes unpleasant. So the Economist’s marketers offered us
a no-
brainer: relative to the print-only option, the print-and-Internet
option looks
clearly superior.
The geniuses at the Economist aren’t the only ones who
understand
the importance of relativity. Take Sam, the television salesman.

He plays the
same general type of trick on us when he decides which
televisions to put
together on display:
36-inch Panasonic for $690
42-inch Toshiba for $850
50-inch Philips for $1,480
Which one would you choose? In this case, Sam knows that
customers find it difficult to compute the value of different
options. (Who
really knows if the Panasonic at $690 is a better deal than the
Philips at
$1,480?) But Sam also knows that given three choices, most
people will
take the middle choice (as in landing your plane between the
runway lights).
So guess which television Sam prices as the middle option?
That’s right—
the one he wants to sell!
Of course, Sam is not alone in his cleverness. The New York
Times ran
a story recently about Gregg Rapp, a restaurant consultant, who
gets paid to
work out the pricing for menus. He knows, for instance, how
lamb sold this
year as opposed to last year; whether lamb did better paired
with squash or
with risotto; and whether orders decreased when the price of the
main
course was hiked from $39 to $41.
One thing Rapp has learned is that high-priced entrées on the
menu

boost revenue for the restaurant—even if no one buys them.
Why? Because
even though people generally won’t buy the most expensive
dish on the
menu, they will order the second most expensive dish. Thus, by
creating an
expensive dish, a restaurateur can lure customers into ordering
the second
most expensive choice (which can be cleverly engineered to
deliver a higher
profit margin).1
SO LET’S RUN through the Economist’s sleight of hand in slow
motion.
As you recall, the choices were:
1. Internet-only subscription for $59.
2. Print-only subscription for $125.
3. Print-and-Internet subscription for $125.
When I gave these options to 100 students at MIT’s Sloan
School of
Management, they opted as follows:
1. Internet-only subscription for $59—16 students
2. Print-only subscription for $125—zero students
3. Print-and-Internet subscription for $125—84 students
So far these Sloan MBAs are smart cookies. They all saw the
advantage in the print-and-Internet offer over the print-only
offer. But were
they influenced by the mere presence of the print-only option
(which I will
henceforth, and for good reason, call the “decoy”). In other

words, suppose
that I removed the decoy so that the choices would be the ones
seen in the
figure below:
Would the students respond as before (16 for the Internet only
and 84
for the combination)?
Certainly they would react the same way, wouldn’t they? After
all, the
option I took out was one that no one selected, so it should
make no
difference. Right?
Au contraire! This time, 68 of the students chose the Internet-
only
option for $59, up from 16 before. And only 32 chose the
combination
subscription for $125, down from 84 before.*
What could have possibly changed their minds? Nothing
rational, I
assure you. It was the mere presence of the decoy that sent 84
of them to the
print-and-Internet option (and 16 to the Internet-only option).
And the
absence of the decoy had them choosing differently, with 32 for
print-and-
Internet and 68 for Internet-only.
This is not only irrational but predictably irrational as well.
Why? I’m
glad you asked.

LET ME OFFER you this visual demonstration of relativity.
As you can see, the middle circle can’t seem to stay the same
size.
When placed among the larger circles, it gets smaller. When
placed among
the smaller circles, it grows bigger. The middle circle is the
same size in
both positions, of course, but it appears to change depending on
what we
place next to it.
This might be a mere curiosity, but for the fact that it mirrors
the way
the mind is wired: we are always looking at the things around us
in relation
to others. We can’t help it. This holds true not only for physical
things—
toasters, bicycles, puppies, restaurant entrées, and spouses—but
for
experiences such as vacations and educational options, and for
ephemeral
things as well: emotions, attitudes, and points of view.
We always compare jobs with jobs, vacations with vacations,
lovers
with lovers, and wines with wines. All this relativity reminds
me of a line
from the film Crocodile Dundee, when a street hoodlum pulls a
switchblade
against our hero, Paul Hogan. “You call that a knife?” says
Hogan
incredulously, withdrawing a bowie blade from the back of his

boot. “Now
this,” he says with a sly grin, “is a knife.”
RELATIVITY IS (RELATIVELY) easy to understand. But
there’s one aspect of
relativity that consistently trips us up. It’s this: we not only
tend to compare
things with one another …
ISS305: Reading Diary Questions
Module #4
4 Total Questions
Q1: Dishonesty and the Tragedy of the Commons [40 points]
Ariely’s findings about dishonesty support the philosophical
conundrum of the “tragedy of the
commons.” First, give us a quick rundown of the tragedy of the
commons (using your own words;
it would be ironic to get dinged for academic dishonesty here).
Now find and describe an
example of the tragedy of the commons that you have
experienced in your life, or that you see
happening in the world. (Note that we also have access to
Wikipedia and the other common
examples, so do spend some time here coming up with
something that is novel/interesting) How
was this tragedy of the commons dealt with? How did the
parties respond? Was the remedy for
this tragedy of the commons effective? How might you apply
Ariely’s findings about dishonesty to
this tragedy of the commons to make the remedy more
effective?

Q2: Behavior Explained [40 points]
Throughout Predictably Irrational, we are presented with
research that shows us that while we
think we are in the driver’s seat of our decisions, we are “pawns
in a game whose forces we
largely fail to comprehend.” For this question, we would like
you to become a behavioral
economist. You will describe a situation where you have seen
people behave in a manner that is
irrational. Then we would like you to design an experiment
which explains this irrationality.
How will you divide participants into a control and treatment
group? What potential issues
might your experiment face? How would you overcome those
issues? And finally, while you
obviously cannot perform your experiment, what answers might
you find that explain why your
subjects behaved so irrationally?
Q3: The Power and Perils of Statistics [30 points]
In your opinion, wherein lies the greatest potential benefit of
statistical inference? That is, are the
greatest advances and gains to be made within the field of
medicine, economics, or some other
field? Why this field? Name at least one specific benefit that
your chosen field might bring in the
near future with the help of statistics, and precisely how
statistics can help. Next, wherein lies the
greatest danger of the abuse of statistical inference? That is, in
what field would such abuse have
the worst consequences? (This may be either the same field
whose potential advances you've
already discussed, or a different field.) Again, why this field?

Name a specific possible abuse of
statistics that you think could lead the field's research astray in
such a way as to have such
consequences.
Q4: Programmatic Self-Evaluation [40 points]
Think about the kinds of program evaluations that would relate
to your life, the comparisons that
might be made between you as you are now and certain
counterfactuals in which one of your
characteristics is changed. For example, you are most likely
seeking a college degree, but how
would certain ‘dependent variables’ – like your life expectancy
or your likely wealth at age 65 –
be changed if you weren't? Wheelan touches on this example, so
come up with two different
characteristics of your own life (ideally, the outcomes of some
of the more important decisions
that you've made in the past, like the decision to seek a degree),
and imagine yourself without
each of these characteristics (one at a time, of course, so as to
isolate the effects of each). Don't
worry about how you would design a test of the effects of each
characteristic – each ‘treatment’ –
but think about what these effects might be. What might the
significant differences be between
yourself and each of your two counterfactuals, in terms of
things like long-term health, long-term
earnings, long-term happiness, etc.? For each of your two
comparisons, make two suggestions as
to what the ‘treatment effects’ of your two real-life

characteristics might be. (You probably won't
be able to be very precise, but that's okay.)

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