Statistics often sound very scientific, so people forget to be analytical when they encounter them. It is easy to use them in ways that can mislead.

Explanation:

The sample used in an inductive inference is relevantly different from the population as a whole.

Keep five simple questions in mind when deciding to accept a position supported by statistics:
(From How To Lie With Statistics, by Darryl Huff.)

• 1) Who says so?
• 2) How do they know?
• 3) What's missing?
• 4) Did somebody change the subject? (Are statistics of comparison really comparable?)
• 5) Does it make sense?

Examples:

(i) To see how Canadians will vote in the next election we polled a hundred people in Calgary. This shows conclusively that the Reform Party will sweep the polls. (People in Calgary tend to be more conservative, and hence more likely to vote Reform, than people in the rest of the country.)

(ii) The apples on the top of the box look good. The entire box of apples must be good. (Of course, the rotten apples are hidden beneath the surface.)

Proof:

Show how the sample is relevantly different from the population as a whole, then show that because the sample is

different, the conclusion is probably different.

More:

This article discusses how statistical methods used in science often lead to error. Odd Are, It's Wrong.