Understanding Data - Data Points: Visualization That Means Something - page 32

next day or for the next week as you pack for a trip, only to find, when the
time comes, that the weather isn't how you expected it to be?

What about the meter in cars that tells you how much farther you can drive
with the current amount of fuel in your tank? I was running errands with my
wife, and the meter said I could drive an estimated 16 more miles, but home
was about 18 miles away. Dilemma. Instead of stopping at the nearest gas
station, I drove toward the one nearest home, and the meter said I had zero
miles left for about 2 miles, but we made it. (Good thing because someone
kept insisting that I would be the one to push the car.)

Weigh yourself more than once, and you might get different readings; typically
though, breathing for a few seconds does not lead to weight loss or gain. The
estimated battery life on your laptop can jump around by hour increments
when only minutes have passed. The subway announcement says a train will
arrive in 10 minutes, but it comes in 11, or a delivery is estimated to arrive on
Monday, but it comes on Wednesday instead.

When you have data that is a series of means and medians or a collection of
estimates based on a sample population, you should always wonder about
the uncertainty.

Note: Numbers seem concrete and absolute,
but estimates carry uncertainity with them.
Data is an abstraction of what it represents,
and the level of exactness varies.

This is especially important when people base major deci-
sions, which affect millions, on estimates, such as with
national and global demographics. Program creation and
funding is often based on these numbers, so even a small
margin of error can make a big difference.

The United States Census Bureau releases data about the country on topics
such as migration, poverty, and housing, which are estimates based on sam-
ples from the population. (This is different from the decennial census, which
aims to count every person in the United States.) A margin of error is provided
with each estimate, which means that the actual count or percentage is likely
within a given range. For example, Figure 1-22 shows estimates about housing.
The margin of error for total households is almost one-quarter of a million.

To put it differently, imagine you have a jar of gumballs that you can't see
into, and you want to guess how many of each color there are. (Why do you
care about gumball distribution? I don't know. Use your imagination. You're
a gumball connoisseur who works for a gumball factory, and you bet your
snotty statistician friend that every jar on your watch is uniformly distributed,
so it's a matter of pride and cash.)