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Science Forum Index » Statistics - Math Forum » Accept the Null Hypothesis...
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| Barry W Brown... |
Posted: Sun Jun 29, 2008 10:51 am |
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Although the supposed discussion of this topic in this group seems to
have degenerated into vituperation
in which I have no desire to participate, I thought the following
observation from a talk by John Tukey at
a talk given in Houston years ago might be of interest.
I had been bothered by the fact that the null hypothesis is usually a
point hypothesis and so cannot be true
(to 10 decimal places, 20 decimal places, etc). Tukey's comment is
paraphrashed as follows.
We know a priori that the null hypothesis is false. However, we do
not have enough evidence to say with
any certitude in which direction it is false. |
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| Luis A. Afonso... |
Posted: Sun Jun 29, 2008 11:58 am |
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Barry W. Brown said
Although the supposed discussion of this topic in this group seems to have degenerated into vituperation in which I have no desire to participate, I thought the following observation from a talk by John Tukey at a talk given in Houston years ago might be of interest. I had been bothered by the fact that the null hypothesis is usually a point hypothesis and so cannot be true (to 10 decimal places, 20 decimal places, etc). Tukey's comment is paraphrashed as follows. We know a priori that the null hypothesis is false. However, we do not have enough evidence to say with any certitude in which direction it is false.
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MY RESPONSE
Not exactly a vain discussion Mr. Brown, because I carefully did support my posts by bibliography (on contrary Jack Tomsky). The main one is the following:
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Ronald A. Fisher succinctly discusses the key point
In relation to any experiment we may speak the ´ null hypotheses ´ and it should be noted that the null hypotheses is never proved or established, but is possibly disproved in the course of experimentation. Every experiment may be said to exist only in order to give the facts a chance of disproving the null hypotheses.
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The famous *** Tucker´s directon *** is not at any worth here: we are ony interested that the parameter value is bounded by 95% say of the set of Confidence Intervals obtained from data from the same Population(s).
Luis
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My Acknowledgments:
Thank you for your concern at this long lasting discussion with Jack Tomsky: do participate every time you think that useful: the more the people the less likeliness to be wrong.
Luis |
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| licas_ at (no spam) hotmail.com... |
Posted: Sun Jun 29, 2008 12:25 pm |
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On 29 Jun, 23:11, JoJo <jojoNOSPAMPLE... at (no spam) americafans.com> wrote:
Quote: Barry W Brown wrote:
Although the supposed discussion of this topic in this group seems to
have degenerated into vituperation
in which I have no desire to participate, I thought the following
observation from a talk by John Tukey at
a talk given in Houston years ago might be of interest.
I had been bothered by the fact that the null hypothesis is usually a
point hypothesis and so cannot be true
Why cannot it be true ?
(to 10 decimal places, 20 decimal places, etc). Tukey's comment is
paraphrashed as follows.
We know a priori that the null hypothesis is false.
Why ?
> However, we do
not have enough evidence to say with
any certitude in which direction it is false.
Did Tukey really mean or say that ?
If I understood Neyman and Pearson correctly, allowing the point
hypothesis to be true is the foundation of the classical hypothesis
testing method. The whole idea is that whatever I say is true, unless
you have good evidence against it. It is similar to situation in court,
where what I say is assumed to be true.
We assume that H0 is true and develop mathematical machinery, formulas,
test statistics etc, assuming just that. If it then leads to unlikely
results, e.g. very low probabilities of occurring the events which have
been observed we have good grounds to reject H0.
We do not know what is the real state of nature, and accept whatever
"sounds OK" or is proposed as H0, we regard it as acceptable until
someone gives evidence against it. This means that if H0: mu=10 is OK
(true?) it may well be that at the same time H0: m=20 is also acceptable
(true?). Why? Because we never know what is the real state of nature and
null hypothesis is just a hypothesis. It is "true" only in that sense,
not as an absolute truth.
If someone doesn't like it this way it means that something is wrong
with this hypothesis testing method and one should be looking for
alternative setup.
JoJo
My response
I agree 100% , this is (by ther words) what Fisher did think.
A very tiny difference is that to suppose H0 true when we perform
the test has (eventualy) the goal to deny it. HOWEVER IF WE ARE UNABLE
ONE CANNOT SAY H0 is true or ACCEPTABLE as Fisher had the
care to say EXPLICITLY.
Luis |
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| JoJo... |
Posted: Sun Jun 29, 2008 5:11 pm |
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Barry W Brown wrote:
Quote: Although the supposed discussion of this topic in this group seems to
have degenerated into vituperation
in which I have no desire to participate, I thought the following
observation from a talk by John Tukey at
a talk given in Houston years ago might be of interest.
I had been bothered by the fact that the null hypothesis is usually a
point hypothesis and so cannot be true
Why cannot it be true ?
Quote: (to 10 decimal places, 20 decimal places, etc). Tukey's comment is
paraphrashed as follows.
We know a priori that the null hypothesis is false.
Why ?
Quote: However, we do
not have enough evidence to say with
any certitude in which direction it is false.
Did Tukey really mean or say that ?
If I understood Neyman and Pearson correctly, allowing the point
hypothesis to be true is the foundation of the classical hypothesis
testing method. The whole idea is that whatever I say is true, unless
you have good evidence against it. It is similar to situation in court,
where what I say is assumed to be true.
We assume that H0 is true and develop mathematical machinery, formulas,
test statistics etc, assuming just that. If it then leads to unlikely
results, e.g. very low probabilities of occurring the events which have
been observed we have good grounds to reject H0.
We do not know what is the real state of nature, and accept whatever
"sounds OK" or is proposed as H0, we regard it as acceptable until
someone gives evidence against it. This means that if H0: mu=10 is OK
(true?) it may well be that at the same time H0: m=20 is also acceptable
(true?). Why? Because we never know what is the real state of nature and
null hypothesis is just a hypothesis. It is "true" only in that sense,
not as an absolute truth.
If someone doesn't like it this way it means that something is wrong
with this hypothesis testing method and one should be looking for
alternative setup.
JoJo |
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| Herman Rubin... |
Posted: Tue Jul 01, 2008 12:00 pm |
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In article <9bf9c3ac-2888-4eb3-82cc-5ca358969da7 at (no spam) y38g2000hsy.googlegroups.com>,
Barry W Brown <brownbar at (no spam) gmail.com> wrote:
Quote: Although the supposed discussion of this topic in this group seems to
have degenerated into vituperation
in which I have no desire to participate, I thought the following
observation from a talk by John Tukey at
a talk given in Houston years ago might be of interest.
I had been bothered by the fact that the null hypothesis is usually a
point hypothesis and so cannot be true
(to 10 decimal places, 20 decimal places, etc). Tukey's comment is
paraphrashed as follows.
We know a priori that the null hypothesis is false. However, we do
not have enough evidence to say with
any certitude in which direction it is false.
The proper approach is to use decision theory; the question is
when to act as if the null hypothesis is true. In many situations,
even a fair difference is tolerable.
So we have to consider a weight measure, which is positive
in a neighborhood of the point null, and negative away,
and for each procedure, integrate the probability that the
procedure will be accepted with respect to this measure.
If the result is positive, "accept" the null, and if
negative, reject it. If there are even a fair number of
observations, the "tail" of the measure will have little effect.
The measure can be looked upon as the utility difference times
the prior, but need not. If the standard deviation of the
usual estimator is large (5 is large, 2 may be okay) compared
to the width of the acceptance region, the integrated weight
measure can be replaced by a point mass at the null; that
approximation, together with robustness results, is discussed
in my paper with Sethuraman, "Bayes Risk Efficiency". in
Sankhya 1965. The general problem, AFAIK, is only discussed
from this standpoint in my paper in the proceedings of the
First Purdue Symposium in 1971; much more needs to be done here.
--
This address is for information only. I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Department of Statistics, Purdue University
hrubin at (no spam) stat.purdue.edu Phone: (765)494-6054 FAX: (765)494-0558 |
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