Page 339 - Statistics for Dummies

Chapter 20: Ten Tips for the Statistically Savvy Sleuth
Histograms
A histogram is a graph that breaks the sample into groups according to a
numerical variable (such as age, height, weight, or income) and shows either
the number of individuals (frequency) or the percentage of individuals (rela-
tive frequency) that fall into each group. Figure 20-1d is a frequency style his-
togram showing the ages of voters in a certain election.
Some items to watch for regarding histograms include the following:
✓ Watch the scale used for the vertical (frequency/relative frequency)
axis, looking especially for results that are exaggerated or played down
through the use of inappropriate scales.
✓ Check out the units on the vertical axis to see whether they report
frequencies or relative frequencies; if they’re relative frequencies, you
need the sample size to determine how much data you’re looking at.
✓ Look at the scale used for the groupings of the numerical variable on the 323
horizontal axis. If the groups are based on small intervals (for example,
0–2, 2–4, and so on), the heights of the bars may look choppy and overly
volatile. If the groups are based on large intervals (for example, 0–100,
100–200, and so on), the data may give a smoother appearance than is
realistic.
Uncover Biased Data
Bias in statistics is the result of a systematic error that either overestimates
or underestimates the true value. For example, if I use a ruler to measure
1
plants and that ruler is ⁄2-inch short, all of my results are biased; they’re
systematically lower than their true values.
Here are some of the most common sources of biased data:
✓ Measurement instruments may be systematically off. For example, a
police officer’s radar gun may say you were going 76 miles per hour
when you know you were only going 72 miles per hour. Or a badly
adjusted scale may always add 5 pounds to your weight.
✓ The way the study is designed can create bias. For example, a survey
question that asks, “Have you ever disagreed with the government?” will
overestimate the percentage of people who are generally unhappy with
the government. (See Chapter 16 for ways to minimize bias in surveys.)
✓ The sample of individuals may not represent the population of
interest — for example, examining student study habits by only going
to the campus library. (See more in the section, “Identify Non-Random
Samples” later in this chapter.)

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