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In Depth Information
Chapter 9: Throwing Statistics a Curve
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