Hi:directly observing the numeric data given above? Say, the confidence interval of the two average salaries overlaps greatly.
A formal way to determine if the effect of a random variable is greater than another is to perform the hypothesis to check whether the difference or ratio of the metric is greater and whether this fact is significant.
However, are there special cases in which one could determine whether the effect of a random variable is greater than that of another without performing the above formal procedure?
For example, when comparing the salary of the domestic and foreign groups, the average salaries and the associated standard errors of the two groups are: (Avg_d, Se_d) and (Avg_f, Se_f). Could we quickly answer the question of greater salary by
On Mon, 12 Jun 2023 08:40:39 -0700 (PDT), Cosine <asecant@gmail.com>directly observing the numeric data given above? Say, the confidence interval of the two average salaries overlaps greatly.
wrote:
Hi:
A formal way to determine if the effect of a random variable is greater than another is to perform the hypothesis to check whether the difference or ratio of the metric is greater and whether this fact is significant.
However, are there special cases in which one could determine whether the effect of a random variable is greater than that of another without performing the above formal procedure?
For example, when comparing the salary of the domestic and foreign groups, the average salaries and the associated standard errors of the two groups are: (Avg_d, Se_d) and (Avg_f, Se_f). Could we quickly answer the question of greater salary by
Hmm. You say "effect" a couple of times, suggesting
something more complicated, before you ask about means.
Means and their SDs are the basis of ordinary t-tests.
"Directly observing" the data? Do you want something like this?
https://www.qimacros.com/hypothesis-testing/tukey-quick-test-excel/
Tukey's Quick Test can be used when:
There are two unpaired samples of similar size that overlap each
other. Ratio of sizes should not exceed 4:3.
One sample contains the highest value, the other sample contains
the lowest value. One sample cannot contain both the highest and the
lowest value, nor can both samples have the same high or low value.
By adding the counts of the number of unmatched points on either end,
one can determine the 5%, 1% and 0.1% critical values as roughly 7,
10, and 13 points.
IIRC, the textbook that first showed me this test quoted Tukey
exactly. Tukey described the test AND its critical values in two
sentences. I was disappointed, a few years later, when I saw
that the newer edition of the textbook had dropped the topic.
If you want a full test on ranks, editors will prefer the K-S test
on ranks.
On Mon, 12 Jun 2023 12:24:53 -0400, Rich Ulrichdirectly observing the numeric data given above? Say, the confidence interval of the two average salaries overlaps greatly.
<rich....@comcast.net> wrote:
On Mon, 12 Jun 2023 08:40:39 -0700 (PDT), Cosine <ase...@gmail.com>
wrote:
Hi:
A formal way to determine if the effect of a random variable is greater than another is to perform the hypothesis to check whether the difference or ratio of the metric is greater and whether this fact is significant.
However, are there special cases in which one could determine whether the effect of a random variable is greater than that of another without performing the above formal procedure?
For example, when comparing the salary of the domestic and foreign groups, the average salaries and the associated standard errors of the two groups are: (Avg_d, Se_d) and (Avg_f, Se_f). Could we quickly answer the question of greater salary by
Hmm. You say "effect" a couple of times, suggesting
something more complicated, before you ask about means.
Means and their SDs are the basis of ordinary t-tests.
"Directly observing" the data? Do you want something like this?
https://www.qimacros.com/hypothesis-testing/tukey-quick-test-excel/ >Tukey's Quick Test can be used when:
There are two unpaired samples of similar size that overlap each
other. Ratio of sizes should not exceed 4:3.
One sample contains the highest value, the other sample contains
the lowest value. One sample cannot contain both the highest and the >lowest value, nor can both samples have the same high or low value.
By adding the counts of the number of unmatched points on either end,
one can determine the 5%, 1% and 0.1% critical values as roughly 7,
10, and 13 points.
IIRC, the textbook that first showed me this test quoted Tukey
exactly. Tukey described the test AND its critical values in two >sentences. I was disappointed, a few years later, when I saw
that the newer edition of the textbook had dropped the topic.
If you want a full test on ranks, editors will prefer the K-S testBy the way -- I remembered the Tukey Quick Test because I
on ranks.
kept it in mind and used it a number of times, for my own
confirmation when browsing data.
I've seen a text book (I forget whose) that had an appendix
with different cutoffs for various pairs of sample Ns. But I
would not suggest trying to publish something relying on it.
I speculate that the "4:3" ratio of Ns (mentioned above) is a
pretty good match to where the cutoffs are exact.
Tukey's two sentences did not specify the ratio of sample sizes,
and called it 'approximate'.
--
Rich Ulrich
I don't recall hearing about this test before. Apparently, it is sometimes called the Tukey-Duckworth (quick) test.
https://en.wikipedia.org/wiki/Tukey%E2%80%93Duckworth_test
On Wednesday, June 14, 2023 at 12:43:15?AM UTC-4, Rich Ulrich wrote:
On Mon, 12 Jun 2023 12:24:53 -0400, Rich Ulrich
"Directly observing" the data? Do you want something like this?By the way -- I remembered the Tukey Quick Test because I
https://www.qimacros.com/hypothesis-testing/tukey-quick-test-excel/
Tukey's Quick Test can be used when:
There are two unpaired samples of similar size that overlap each
other. Ratio of sizes should not exceed 4:3.
One sample contains the highest value, the other sample contains
the lowest value. One sample cannot contain both the highest and the
lowest value, nor can both samples have the same high or low value.
By adding the counts of the number of unmatched points on either end,
one can determine the 5%, 1% and 0.1% critical values as roughly 7,
10, and 13 points.
IIRC, the textbook that first showed me this test quoted Tukey
exactly. Tukey described the test AND its critical values in two
sentences. I was disappointed, a few years later, when I saw
that the newer edition of the textbook had dropped the topic.
If you want a full test on ranks, editors will prefer the K-S test
on ranks.
kept it in mind and used it a number of times, for my own
confirmation when browsing data.
I've seen a text book (I forget whose) that had an appendix
with different cutoffs for various pairs of sample Ns. But I
would not suggest trying to publish something relying on it.
I speculate that the "4:3" ratio of Ns (mentioned above) is a
pretty good match to where the cutoffs are exact.
Tukey's two sentences did not specify the ratio of sample sizes,
and called it 'approximate'.
On Wed, 28 Jun 2023 11:27:08 -0700 (PDT), Bruce Weaver
<bweaver@lakeheadu.ca> wrote:
I don't recall hearing about this test before. Apparently, it is
sometimes called the Tukey-Duckworth (quick) test.
https://en.wikipedia.org/wiki/Tukey%E2%80%93Duckworth_test
Top-posting? Okay.
Okay. It adds that Duckworth requested a simple test, usable in
the field, and this is what Tukey provided. I'm not surprised if he
gave us some other Quick tests -- so someone added Duckworth?
Tukey was a prolific statistican, with a different perspective from
most of us. I gained useful insights from reading his textbooks,
though I still wonder if they are 'simple' enough to be used in
the intro courses they are written for. I think I got much of my
perspective on the proper use of transformations from his chapters
on the subject.
There is some paper on presenting data with useful graphics (IIRC
the topic rightly) which lists Tukey, whose ideas it presented, as
author #9; a statistician friend said that his professors had referred
to it as "et al. and Tukey" .
On Wednesday, June 14, 2023 at 12:43:15?AM UTC-4, Rich Ulrich wrote:
On Mon, 12 Jun 2023 12:24:53 -0400, Rich Ulrich
< snip, original problem >
the >> >lowest value, nor can both samples have the same high or lowhttps://www.qimacros.com/hypothesis-testing/tukey-quick-test-excel/"Directly observing" the data? Do you want something like this?
Tukey's Quick Test can be used when: >> >
There are two unpaired samples of similar size that overlap each
other. Ratio of sizes should not exceed 4:3.
One sample contains the highest value, the other sample contains
the lowest value. One sample cannot contain both the highest and
value. >> >
end, >> >one can determine the 5%, 1% and 0.1% critical values asBy adding the counts of the number of unmatched points on either
roughly 7, >> >10, and 13 points.
test >> >on ranks.
IIRC, the textbook that first showed me this test quoted Tukey
exactly. Tukey described the test AND its critical values in two
sentences. I was disappointed, a few years later, when I saw
that the newer edition of the textbook had dropped the topic.
If you want a full test on ranks, editors will prefer the K-S
By the way -- I remembered the Tukey Quick Test because I
kept it in mind and used it a number of times, for my own
confirmation when browsing data.
I've seen a text book (I forget whose) that had an appendix
with different cutoffs for various pairs of sample Ns. But I
would not suggest trying to publish something relying on it.
I speculate that the "4:3" ratio of Ns (mentioned above) is a
pretty good match to where the cutoffs are exact.
Tukey's two sentences did not specify the ratio of sample sizes,
and called it 'approximate'.
Rich Ulrich wrote:
On Wed, 28 Jun 2023 11:27:08 -0700 (PDT), Bruce Weaver
<bweaver@lakeheadu.ca> wrote:
I don't recall hearing about this test before. Apparently, it is
sometimes called the Tukey-Duckworth (quick) test.
https://en.wikipedia.org/wiki/Tukey%E2%80%93Duckworth_test
Top-posting? Okay.
Okay. It adds that Duckworth requested a simple test, usable in
the field, and this is what Tukey provided. I'm not surprised if he
gave us some other Quick tests -- so someone added Duckworth?
Tukey was a prolific statistican, with a different perspective from
most of us. I gained useful insights from reading his textbooks,
though I still wonder if they are 'simple' enough to be used in
the intro courses they are written for. I think I got much of my
perspective on the proper use of transformations from his chapters
on the subject.
There is some paper on presenting data with useful graphics (IIRC
the topic rightly) which lists Tukey, whose ideas it presented, as
author #9; a statistician friend said that his professors had referred
to it as "et al. and Tukey" .
On Wednesday, June 14, 2023 at 12:43:15?AM UTC-4, Rich Ulrich wrote:
On Mon, 12 Jun 2023 12:24:53 -0400, Rich Ulrich
< snip, original problem >
the >> >lowest value, nor can both samples have the same high or lowhttps://www.qimacros.com/hypothesis-testing/tukey-quick-test-excel/"Directly observing" the data? Do you want something like this?
Tukey's Quick Test can be used when: >> >
There are two unpaired samples of similar size that overlap each
other. Ratio of sizes should not exceed 4:3.
One sample contains the highest value, the other sample contains
the lowest value. One sample cannot contain both the highest and
value. >> >
end, >> >one can determine the 5%, 1% and 0.1% critical values asBy adding the counts of the number of unmatched points on either
roughly 7, >> >10, and 13 points.
test >> >on ranks.
IIRC, the textbook that first showed me this test quoted Tukey
exactly. Tukey described the test AND its critical values in two
sentences. I was disappointed, a few years later, when I saw
that the newer edition of the textbook had dropped the topic.
If you want a full test on ranks, editors will prefer the K-S
By the way -- I remembered the Tukey Quick Test because I
kept it in mind and used it a number of times, for my own
confirmation when browsing data.
I've seen a text book (I forget whose) that had an appendix
with different cutoffs for various pairs of sample Ns. But I
would not suggest trying to publish something relying on it.
I speculate that the "4:3" ratio of Ns (mentioned above) is a
pretty good match to where the cutoffs are exact.
Tukey's two sentences did not specify the ratio of sample sizes,
and called it 'approximate'.
A problem seems to be in "One sample cannot contain both the highest
and the lowest value, nor can both samples have the same high or low
value."
Is a test a test, if you can't always apply it?
Is there some action
advised if the test can't be applied?
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