At 12:44 PM 4/10/00 -0300, Robert Dawson wrote:
> Moreover, in cases in which the null
>hypothesis has any prior credibility - as should always be the case - that
>"something" (eg, amount by which the IQ of the subject population differs
>from the standardized value of 100) is usually a sensible thing to study.
>And that thing can usually be thought of as "effect size".
why does the null have to have prior credibility? comparing two treatments
where the null is NO difference makes little in the way of credible sense
... it is a made up value ... let's assume that the treatments are the same
... why would one want to nor need to think this?
if you are interested in the relationship between heights and weights of
people, in the larger population ... the notion that we test this against a
null of rho=0 is not credible ... in fact, it is rather stupid ... a more
sensible null would be perhaps a rho of .5 ...
now, in some instances we have a more logical null ... the case of 100 for
IQ is logical in the sense that this is what test publishers have said is
the way they have normed the test ... and if one thinks that ability has
gone up or down ... 100 seems a reasonable null to offer as the staw dog
... it is sensible and credible AS a null
but many are NOT credible in any SENSIBLE way ...
finally, if we test some null of 0 in some given situation ... we do not
have any particular effect size (NON 0 null) in mind ... though maybe we
should ... but even if that is the case .. the fact that we create a null
and test a null does NOT imply that we are therefore testing some effect
size ...
and, if we were interested in an effect size, then we don't have to test
for it ... but we could ask the question: how large is it? that is NOT a
test of a hypothesis
we don't need ANY null to find answers to questions of import that we might
have
> In the classic student blooper cases: "H0: mu = x bar" and "H0: mu
>equals the nearest round number to x bar" it isn't: and those tests should
>not be done.
>
> -Robert
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