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Name: Tierra H.
Status: student	
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Date: 5/18/2004

If a finding is statistically significant, why is it also necessary to consider practical significance?

One has to be aware that statistics is a discipline for handling our ignorance. If we know that A causes B, or A and B are correlated (that is, if A changes by so much, then B changes by some amount), we do not need statistics. The language of statistics, especially regarding cause and effect, is very precise, very careful, and a bit daunting.

For example: A fictional statistician might say something like, "If repeated measurement of some variable, X, is normally and randomly distributed about the mean, M, with standard deviation, S, then with a confidence level of 0.YZ (usually 0.95 or 0.99) we can say that any values of X that differ from the mean, M, may be explained by random fluctuations in the variable, X, and hence are not statistically significant.

Now there are a lot of "if" conditions in such a statement: Differences are normally and randomly distributed about M means there are no more than one "hump" in the measurements, that the difference between X and M MAY be due to random fluctuations alone, and some more hidden "ifs". There are also other types of distributions that do not have a "bell-shaped" curve like the above. What statistics does is provide us with a "warning" that something MAY be happening with the variable X that makes its value "significantly" different than M. It's not a certainty. If X differs from M more than what random fluctuations can "explain" then it says, "Hey, you better check this out!" You do not have to, it is just a consistent way of identifying when some X is significantly different than the average, M.

Vince Calder

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