We don't really disagree. Any apparent disagreement is probably due
to the abbreviated kind of discussion that takes place in Usenet.
See http://www.tufts.edu/~gdallal/onesided.htm
Alan McLean ([EMAIL PROTECTED]) wrote:
My point however is still true - that the person who receives
the
I agree that it's the detail about which we disagree! However, one
detail is pretty important - I still think you are confusing the trial
and the statistical test. The same confusion is shown on the web site.
I agree totally that if the treatment appears to be significantly worse
than the
On 13 Mar 2001 16:32:15 -0500, [EMAIL PROTECTED] (Herman
Rubin) wrote:
In article [EMAIL PROTECTED],
RD [EMAIL PROTECTED] wrote:
On 13 Mar 2001 07:12:33 -0800, [EMAIL PROTECTED] (dennis roberts) wrote:
1. some test statistics are naturally (the way they work anyway) ONE sided
with respect to
In article [EMAIL PROTECTED],
Alan McLean [EMAIL PROTECTED] wrote:
[EMAIL PROTECTED] wrote:
More importantly, I would say: DON'T DO TESTS. Instead, try to find
models that you would be prepared to use to predict the response
in as-yet untried circumstances.
--
Hypothesis testing is
On 13 Mar 2001 14:23:04 -0800, [EMAIL PROTECTED] (dennis roberts) wrote:
well, help me out a bit
i give a survey and ... have categorized respondents into male and females
... and also into science major and non science majors ... and find a data
table like:
MTB chisquare c1 c2
Chi-Square
Will Hopkins wrote:
Responses to various folks. And to everyone touchy about one-tailed
tests, let me make it quite clear that I am only promoting them as a
way of making a sensible statement about probability. A two-tailed p
value has no real meaning, because no real effects are ever
we have to first separate out 2 things:
1. some test statistics are naturally (the way they work anyway) ONE sided
with respect to retain/reject decisions
example: chi square test for independence ... we reject ONLY when chi
square is LARGER than some CV ... to put a CV at the lower end of
dennis roberts wrote:
we have to first separate out 2 things:
1. some test statistics are naturally (the way they work anyway) ONE sided
with respect to retain/reject decisions
example: chi square test for independence ... we reject ONLY when chi
square is LARGER than some CV ... to
In article p0433010fb6d329af7d2d@[139.80.121.126],
Will Hopkins [EMAIL PROTECTED] wrote:
At 7:34 PM + 12/3/01, Jerry Dallal wrote:
Don't do one-tailed tests.
If you are going to do any tests, it makes more sense to one-tailed
tests. The resulting p value actually means something that folks
Will Hopkins wrote:
At 7:34 PM + 12/3/01, Jerry Dallal wrote:
Don't do one-tailed tests.
If you are going to do any tests, it makes more sense to one-tailed
tests.
If you're doing a 1 tailed test, why test at all? Just switch from
standard treatment to the new one. Can't do any
On 13 Mar 2001 07:12:33 -0800, [EMAIL PROTECTED] (dennis roberts) wrote:
1. some test statistics are naturally (the way they work anyway) ONE sided
with respect to retain/reject decisions
example: chi square test for independence ... we reject ONLY when chi
square is LARGER than some CV ...
well, help me out a bit
i give a survey and ... have categorized respondents into male and females
... and also into science major and non science majors ... and find a data
table like:
MTB chisquare c1 c2
Chi-Square Test: C1, C2
Expected counts are printed below observed counts
In article [EMAIL PROTECTED],
RD [EMAIL PROTECTED] wrote:
On 13 Mar 2001 07:12:33 -0800, [EMAIL PROTECTED] (dennis roberts) wrote:
1. some test statistics are naturally (the way they work anyway) ONE sided
with respect to retain/reject decisions
example: chi square test for independence ...
In article [EMAIL PROTECTED],
RD [EMAIL PROTECTED] writes:
On 13 Mar 2001 07:12:33 -0800, [EMAIL PROTECTED] (dennis roberts) wrote:
1. some test statistics are naturally (the way they work anyway) ONE sided
with respect to retain/reject decisions
example: chi square test for
Responses to various folks. And to everyone touchy about one-tailed
tests, let me make it quite clear that I am only promoting them as a
way of making a sensible statement about probability. A two-tailed p
value has no real meaning, because no real effects are ever null. A
one-tailed p
Hi, all,
We are testing a group of subjects on their performance in two different
conditions (say, A and B), and we are testing them individually. We have an
alternative hypothesis that reaction time in condition A should be longer
than in condition B, so we perform a one-tailed t test. However,
auda wrote:
Hi, all,
We are testing a group of subjects on their performance in two different
conditions (say, A and B), and we are testing them individually. We have an
alternative hypothesis that reaction time in condition A should be longer
than in condition B, so we perform a
auda wrote:
Hi, all,
We are testing a group of subjects on their performance in two different
conditions (say, A and B), and we are testing them individually. We have an
alternative hypothesis that reaction time in condition A should be longer
than in condition B, so we perform a
At 7:34 PM + 12/3/01, Jerry Dallal wrote:
Don't do one-tailed tests.
If you are going to do any tests, it makes more sense to one-tailed
tests. The resulting p value actually means something that folks can
understand: it's the probability the true value of the effect is
opposite to what
On Tue, 13 Mar 2001, Will Hopkins wrote in part:
Example: you observe an effect of +5.3 units, one-tailed p = 0.04.
Therefore there is a probability of 0.04 that the true value is less
than zero.
Sorry, that's incorrect. The probability is 0.04 that you would find an
effect as large as
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