Hi,
I agree with nick's point.
Is it right to say this?
1. The alpha is the probability of making a type 1 error i.e the probability
of rejecting a null hypothesis that is actually true.
2. Since we have to compare apples to apples and not oranges. The fact that
we compare the p value to the alpha means that the p value is also the
probability of rejecting a null hypothesis that is actually true.
3. However, the difference between the two is that alpha is prespecified
while the p value that you see in the output emerges from the data.
rajesh
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Nick Cox
Sent: 17 January 2007 16:07
To: [email protected]
Subject: RE: st: RE: joint significant
Note that this is wrong. The P-value is emphatically not
the probability that the null hypothesis is true.
The P-value is the probability of getting results as or
more extreme than those observed _if_ the null hypothesis
is true and if the associated assumptions are correct.
In any case, the picture here that there is a spike of probability
corresponding to a zero test statistic does not match basic
facts about the sampling distribution, which in this kind of
problem is continuous.
Nick
[email protected]
White, Justin
> Here is how to interpret a p-value. Let's say you have a p-value of
> 0.0890 from an F-test. This tells us that given the data
> sample, we can
> expect the estimated coefficients to be jointly equal to zero in 8.9
> times out of 100. This is known as Type-1 error.
>
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