In article <[EMAIL PROTECTED]>,
dennis roberts <[EMAIL PROTECTED]> wrote:
>say ... we have some concern about whether the average IQ is still 100 in
>the general population ... as test publishers say (of course, herman rubin
>would say that we know that this null is NOT really (exactly) true ... but,
>let's put this aside for the moment)
>
>now, we take (if this is really possible) a random sample of 100 ... do a
>simple t test with the null being 100 and ...
>
>the p value comes out to be .08 ... TEST A
>
>or, this test turned up a p value of .02 ... TEST B
>
>or, this test turned up a p value of .008 ... TEST C
>
>assume for a moment that we did things correctly in terms of sampling, run
>the right test, assumptions met, etc.
>
>are we able to accurately say ... that if we had the results as in TEST C
>... that we are more convinced that the null of 100 is NOT true ... than
>for TEST B or TEST A?
>
>no ifs, ands, or buts or it depends on this or that ... just straight
>talking ... can we say this?
Of course you can. I must admit to being baffled as to why anyone
would think this is a controversial matter.
Of course, there are many arguments for why p-values aren't actually
a very *good* measure of the strength of evidence against the null.
Use something else if you like. But if you're going to use p-values,
what could you possibly think you are doing if you *aren't* going to
interpret the result as being stronger evidence against the null the
smaller the p-value is?
Radford Neal
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Radford M. Neal [EMAIL PROTECTED]
Dept. of Statistics and Dept. of Computer Science [EMAIL PROTECTED]
University of Toronto http://www.cs.utoronto.ca/~radford
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