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RE: st: RE: joint significant
I am sorry for the misinformation. After I read Nick's response, it
jogged my memory from Intro to Econometrics. It's been a while. Sorry.
What I should have said is this:
Let's say you have a p-value of 0.0890 from an F-test. This tells us
that we can expect coefficients as extreme as observed in 8.9 out of 100
random samples given the null hypothesis is true. We can use this
information to either reject or fail to reject the null based on
personal confidence criterion. In this case we can reject the null at a
91.1% confidence interval.
Justin White
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Rajesh
Tharyan
Sent: Wednesday, January 17, 2007 11:31 AM
To: [email protected]
Subject: RE: st: RE: joint significant
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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