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Part I: Vital Statistics about Statistics
America). This “plus or minus” that you add to your sample statistic in order
to estimate a parameter is the margin of error.
When you take a sample statistic (such as the sample mean or sample per-
centage) and add/subtract a margin of error, you come up with what statisti-
cians call a confidence interval. A confidence interval represents a range of
likely values for the population parameter, based on your sample statistic.
For example, suppose the average time it takes you to drive to work each day
is 35 minutes, with a margin of error of plus or minus 5 minutes. You estimate
that the average time to work would be anywhere from 30 to 40 minutes. This
estimate is a confidence interval.
Some confidence intervals are wider than others (and wide isn’t good,
because it equals less accuracy). Several factors influence the width of a con-
fidence interval, such as sample size, the amount of variability in the popula-
tion being studied, and how confident you want to be in your results. (Most
researchers are happy with a 95% level of confidence in their results.) For
more on factors that influence confidence intervals, as well as instructions for
calculating and interpreting confidence intervals, see Chapter 13.
Hypothesis testing
Hypothesis test is a term you probably haven’t run across in your everyday
dealings with numbers and statistics. But I guarantee that hypothesis tests
have been a big part of your life and your workplace, simply because of the
major role they play in industry, medicine, agriculture, government, and a
host of other areas. Any time you hear someone talking about their study
showing a “statistically significant result,” you’re encountering a hypothesis
test. (A statistically significant result is one that is unlikely to have occurred
by chance. See Chapter 14 for the full scoop.)
Basically, a hypothesis test is a statistical procedure in which data are col-
lected from a sample and measured against a claim about a population
parameter. For example, if a pizza delivery chain claims to deliver all pizzas
within 30 minutes of placing the order, on average, you could test whether
this claim is true by collecting a random sample of delivery times over a cer-
tain period and looking at the average delivery time for that sample. To make
your decision, you must also take into account the amount by which your
sample results can change from sample to sample (which is related to the
margin of error).
Because your decision is based on a sample and not the entire population, a
hypothesis test can sometimes lead you to the wrong conclusion. However,
statistics are all you have, and if done properly, they can give you a good
chance of being correct. For more on the basics of hypothesis testing, see
Chapter 14.

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