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In Depth Information

**Chapter 9: Throwing Statistics a Curve**

Chapter 9

Throwing Statistics a Curve

In This Chapter

▶
Understanding key terms used in statistics

▶
Testing for central tendencies in a data sample

▶
Analyzing deviation in a data sample

▶
Looking for similarities in two data samples

▶
Analyzing by bins and percentiles

▶
Counting items in a data sample

J
ust pick up the newspaper or turn on the television or the radio. We’re

bombarded with interesting facts and figures that are the result of

statistical work: There is a 60 percent chance of rain; the Dow Jones Industrial

Average gained 2.8 percent; the Yankees are favored over the Red Sox, 4-3;

and so on.

Statistics are used to tell us facts about the world around us. Statistics are

also used to give us lies about our world. Statistics can be used to confuse or

obscure information. Imagine you try a new candy bar and you like it. Well,

then you can boast that 100 percent of the people who tried it liked it!

Sometimes statistics produce odd conclusions — to say the least! Imagine

this: Bill Gates helps at a homeless shelter. The average wealth of the 40 or so

people in the room is $1 billion. Why? Because Bill’s worth is counted in the

average, thereby skewing the average past the point of making sense. How

about this: You hear on the news that the price of gasoline dropped 6

percent. Hurray! Let’s go on a trip. But what is that 6 percent decrease based on?

Is it a comparison to last week’s price, last month’s price, or last year’s price?

Perhaps the price of gasoline dropped 6 percent, compared to last month.

But still prices are 20 percent higher than last year. Is this good news?

Statistics are traditionally divided into two types.
Descriptive statistics,

covered in this chapter, help you to summarize and understand data.
Inferential

statistics,
covered in Chapter 10, are used to draw conclusions about data

comparisons.