Datasheet

10
Part I: Statistics and Excel: A Marriage Made in Heaven
By shouldering the number-crunching load, software increases our speed of
traveling down that path. Some software packages are specialized for statisti-
cal analysis and contain many of the tools that statisticians use. Although
not marketed specifically as a statistical package, Excel provides a number of
these tools, which is why I wrote this book.
I said that number crunching is a small part of the path to sound decisions.
The most important part is the concepts statisticians work with, and that’s
what I talk about for most of the rest of this chapter.
Samples and populations
On election night, TV commentators routinely predict the outcome of elec-
tions before the polls close. Most of the time they’re right. How do they
do that?
The trick is to interview a sample of voters after they cast their ballots.
Assuming the voters tell the truth about whom they voted for, and assuming
the sample truly represents the population, network analysts use the sample
data to generalize to the population of voters.
This is the job of a statistician — to use the findings from a sample to make a
decision about the population from which the sample comes. But sometimes
those decisions don’t turn out the way the numbers predicted. History buffs
are probably familiar with the memorable picture of President Harry Truman
holding up a copy of the Chicago Daily Tribune with the famous, but wrong,
headline “Dewey Defeats Truman” after the 1948 election. Part of the statisti-
cian’s job is to express how much confidence he or she has in the decision.
Another election-related example speaks to the idea of the confidence in
the decision. Pre-election polls (again, assuming a representative sample of
voters) tell you the percentage of sampled voters who prefer each candidate.
The polling organization adds how accurate they believe the polls are. When
you hear a newscaster say something like “accurate to within three percent,”
you’re hearing a judgment about confidence.
Here’s another example. Suppose you’ve been assigned to find the average
reading speed of all fifth-grade children in the U.S., but you haven’t got the
time or the money to test them all. What would you do?
Your best bet is to take a sample of fifth-graders, measure their reading
speeds (in words per minute), and calculate the average of the reading
speeds in the sample. You can then use the sample average as an estimate of
the population average.
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