Date: Tue, 8 Apr 2008 08:23:46 -0500
Reply-To: "Peck, Jon" <peck@spss.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: "Peck, Jon" <peck@spss.com>
Subject: Re: Tests of "significance"
In-Reply-To: A<021501c89930$7a9febc0$9c00a8c0@NOTEBOOK>
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". For a small effect, you can always increase
sample size enough to get a significant result"
Well, yes, but there is nothing wrong with this. The probabilities are what they are. The question is only what you do then. It has been amply discussed that statistical significance and substantive significance are not the same thing.
Regards,
Jon Peck
-----Original Message-----
From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of Hector Maletta
Sent: Monday, April 07, 2008 10:24 PM
To: SPSSX-L@LISTSERV.UGA.EDU
Subject: Re: [SPSSX-L] Tests of "significance"
Samir,
The probability resulting from a significance test is an increasing function
of two things: the size of the effect or difference observed in the sample,
and the size of the sample. For a small effect, you can always increase
sample size enough to get a significant result. For a given sample size,
there is (almost) always an effect big enough to be statistically
significant (i.e. so big that you are 95% confident it is different from
zero in the population).
Hector
-----Original Message-----
From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of
<Samir Kr Paul>
Sent: 08 April 2008 00:31
To: SPSSX-L@LISTSERV.UGA.EDU
Subject: Re: Tests of "significance"
Dear Hector:
Just wondering abt one point - how did u assume that the sample Bob is
working on is too small. He hasnt mention it yet. He has talked abt
neither the universe nor the sample.
I certainly aggree with your statement on small sample against a large
universe. But what if the sample is considerable sufficient and
representative?
Cant we say 'the variables have certain association in as many as 95%
cases' if the chi-square value comes out to be significant at .05 level
from the contigency table on the two variables he is concerned with.
Please put some more insights on it if i am not correct to any extent.
Regards,
Sam
> You could say that any difference observed might possibly be due to mere
> chance. You do not have sufficient grounds to affirm that a relationship
> or
> difference actually exists in the population, based on the relationship
> observed in your sample, because the observed difference or relationship
> is
> too weak for the size of your sample, or the size of your sample is too
> small for such a weak relationship. You need a larger sample, or a
> stronger
> relationship/difference, or both.
>
> Notice also that this concerns your ability to infer from your sample to
> the
> population, and has nothing to do with the substantive significance of
> your
> hypothesis. Even a very small (and thus substantively "insignificant")
> effect may be found to be statistically significant if the sample is
> sufficiently large.
>
> Hector
>
> -----Original Message-----
> From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of
> Bob
> Schacht
> Sent: 07 April 2008 23:09
> To: SPSSX-L@LISTSERV.UGA.EDU
> Subject: Tests of "significance"
>
> Please help me out here. The cautious, humble statistician says, "At the
> .05 level, the Null Hypothesis is rejected." To the man on the street,
> this
> is just pedantic mumbo-jumbo. So, say I'm using SPSS to do a Chi-square on
> responses to a Likert scale question by case outcome. If the Chi-square
> comes out with p< .05, I say, somewhat formally, "At the .05 level, the
> Null Hypothesis that case outcome and responses to this question are
> independent, is rejected."
>
> How can I translate that into plain English that the proverbial man on the
> street can understand, while remaining statistically correct?
>
> I am looking for a generic phrase that can be used for all similar
> statistical tests based on a null hypothesis of independence.
>
> Thanks,
> Bob
>
>
> Robert M. Schacht, Ph.D. <schacht@hawaii.edu>
> Pacific Basin Rehabilitation Research & Training Center
> 1268 Young Street, Suite #204
> Research Center, University of Hawaii
> Honolulu, HI 96814
>
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