Made to Stick

tionship. It’s more important for people to remember the relationship
than the number.
T h e   H u m a n - S c a l e   Pr i n c i p l e
Another way to bring statistics to life is to contextualize them in terms
that are more human, more everyday. As a scientific example, con-
trast the following two statements:
1. Scientists recently computed an important physical con-
straint to an extraordinary accuracy. To put the accuracy in
perspective, imagine throwing a rock from the sun to the
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earth and hitting the target within one third of a mile of
dead center.
2. Scientists recently computed an important physical con-
straint to an extraordinary accuracy. To put the accuracy in
perspective, imagine throwing a rock from New York to Los
Angeles and hitting the target within two thirds of an inch
of dead center.
Which statement seems more accurate?
As you may have guessed, the accuracy levels in both questions
are exactly the same, but when different groups evaluated the two
statements, 58 percent of respondents ranked the statistic about the
sun to the earth as “very impressive.” That jumped to 83 percent for
the statistic about New York to Los Angeles. We have no human ex-
perience, no intuition, about the distance between the sun and the
earth. The distance from New York to Los Angeles is much more tan-
gible. (Though, frankly, it’s still far from tangible. The problem is
that if you make the distance more tangible—like a football field—
then the accuracy becomes intangible. “Throwing a rock the dis-
tance of a football field to an accuracy of 3.4 microns” doesn’t help.)
Stephen Covey, in his book The 8th Habit, describes a poll of
23,000 employees drawn from a number of companies and indus-
tries. He reports the poll’s findings:
•
Only 37 percent said they have a clear understanding of
what their organization is trying to achieve and why.
•
Only one in five was enthusiastic about their team’s and
their organization’s goals.
•
Only one in five said they had a clear “line of sight” between
their tasks and their team’s and organization’s goals.
•
Only 15 percent felt that their organization fully enables
them to execute key goals.
•
Only 20 percent fully trusted the organization they work for.
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Pretty sobering stuff. It’s also pretty abstract. You probably walk
away from these stats thinking something like “There’s a lot of dissat-
isfaction and confusion in most companies.”
Then Covey superimposes a very human metaphor over the sta-
tistics. He says, “If, say, a soccer team had these same scores, only 4 of
the 11 players on the field would know which goal is theirs. Only 2 of
the 11 would care. Only 2 of the 11 would know what position they
play and know exactly what they are supposed to do. And all but 2
players would, in some way, be competing against their own team
members rather than the opponent.”
The soccer analogy generates a human context for the statistics. It
creates a sense of drama and a sense of movement. We can’t help but
imagine the actions of the two players trying to score a goal, being op-
posed at every stage by the rest of their team.
Why does the analogy work? It relies on our schema of soccer
teams and the fact that this schema is somehow cleaner, more well-
defined, than our schemas of organizations. It’s more vivid to think of
a lack of cooperation on a soccer team—where teamwork is para-
mount—than in a corporation. And this is exactly Covey’s point: Cor-
porations should operate like teams, but they don’t. Humanizing the
statistics gives the argument greater wallop.
As another example of the human-scale principle, take a mun-
dane situation: figuring out whether a particular technological up-
grade is worth the money. One example comes from Cisco, when it
had to decide whether to add a wireless network for its employees.
The cost of maintaining a wireless network was estimated at $500 per
year per employee. That price sounds hefty—on the order of adding
dental or vision insurance for all employees. But it’s not a benefit, it’s
an investment. So how do you compute the value of an investment?
You’ve got to decide whether you can get $501 worth of additional
value from each employee each year after adding the network.
One Cisco employee figured out a better way to think about the
investment: “If you believe you can increase an employee’s produc-
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tivity by one to two minutes a day, you’ve paid back the cost of wire-
less.” On this scale, the investment is much easier to assess. Our intu-
ition works at this scale. We can easily simulate scenarios where
employees can save a few minutes from wireless access—for instance,
sending someone a request for a forgotten document during a critical
meeting.
Statistics aren’t inherently helpful; it’s the scale and context that
make them so. Not many people have an intuition about whether
wireless networking can generate $500 worth of marginal value per
employee per year. The right scale changes everything. We saw that
Concreteness allows people to bring their knowledge to bear—
remember HP’s simulation of a family at Disney World? Similarly,
the human-scale principle allows us to bring our intuition to bear in
assessing whether the content of a message is credible.
S
tatistics are a good source of internal credibility when they are
used to illustrate relationships. In the introduction of this book,
we discussed the example of the CSPI’s campaign against saturated-
fat-loaded movie popcorn. The relevant statistic was that a medium-
sized bag of popcorn had 37 grams of saturated fat. So what? Is that
good or bad?
Art Silverman, of the CSPI, cleverly placed the popcorn’s
saturated-fat content in a relevant context for comparison. He said
that one bag of popcorn was equivalent to a whole day’s worth of un-
healthy eating. Silverman knew that most people would be appalled
by this finding.
What if Silverman had been a sleazebag? He could have picked a
food item that was notoriously unhealthy but relatively low in satu-
rated fat, such as lollipops. “One bag of popcorn has the fat equiva-
lent of 712,000 lollipops!” (Or an infinite number of lollipops, since
they’re fat-free.) This statistic is sleazy because it draws its power from
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sleight of hand involving different senses of unhealthy food. A sleazy
movie-theater executive, to retaliate, might have changed the do-
main of comparison from saturated fat to some positive attribute of
corn: “A bag of popcorn has as much Vitamin J as 71 pounds of broc-
coli!” (We made this up.)
These possibilities are examples of why writing about statistics
filled us with anxiety. Particularly in the realm of politics, tinkering
with statistics provides lucrative employment for untold numbers of
issue advocates. Ethically challenged people with lots of analytical
smarts can, with enough contortions, make almost any case from a
given set of statistics.
Of course, let’s also remember that it’s easier to lie without statis-
tics than with them. Data enforces boundaries. Unless people are un-
ethical enough to make up data, the reality of the data constrains
them. That’s a good thing, but it still leaves a lot of wiggle room.
So what about the rest of us, who aren’t spinmeisters? What do we
do? We will still be tempted to put the best possible spin on our sta-
tistics. All of us do it. “I scored sixteen points for the church basketball
team tonight!” (Not mentioned: twenty-two missed shots and the loss
of the game.) “I’m five feet six.” (Not mentioned: The three-inch
heels.) “Revenue was up 10 percent this year, so I think I deserve a
bonus.” (Not mentioned: Profits tanked.)
When it comes to statistics, our best advice is to use them as in-
put, not output. Use them to make up your mind on an issue. Don’t
make up your mind and then go looking for the numbers to support
yourself—that’s asking for temptation and trouble. But if we use sta-
tistics to help us make up our minds, we’ll be in a great position to
share the pivotal numbers with others, as did Geoff Ainscow and the
Beyond War supporters.
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D e a l i n g   w i t h   S h a r k   A t t a c k   H y s t e r i a
T H E   S I T UAT I O N: