Will Hopkins wrote:
>
> I accept that there are unusual cases where the null hypothesis has a
> finite probability of being be true, but I still can't see the point in
> hypothesizing a null, not in biomedical disciplines, anyway.
>
> If only we could replace the p value with a probability that the true
> effect is negative (or has the opposite sign to the observed effect). The
> easiest way would be to insist on one-tailed tests for everything. Then
> the p value would mean exactly that. An example of two wrongs making a right.
No, a one-tailed test doesn't work; it is still computed using the null
value. To find what you want you need Bayesian techniques... but then
(if your prior distribution is valid) you can answer the question you
*really* wanted to answer - "what is the probability that the effect
exists?"
Or even "what is the distribution of the parameter value?"
-Robert Dawson
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