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Dennis Roberts asked, imagining a testing-free universe:
what would the vast majority of folks who either do inferential work
and/or
teach it ... DO
what analyses would they be doing? what would they be teaching?
I wrote:
* students would be told in their compulsory intro stats that
On Fri, 7 Apr 2000, Chris Mecklin wrote:
Among other things
My point is that I want to show my class an example where they can see the
pitfalls of making a decision based solely on a p-value. I don't want
My favorite, not contrived example, has to do with vocational advice and
gender. It
On 7 Apr 2000, dennis roberts wrote:
i was not suggesting taking away from our arsenal of tricks ... but, since
i was one of those old guys too ... i am wondering if we were mostly lead
astray ...?
the more i work with statistical methods, the less i see any meaningful (at
the level of
On Fri, 7 Apr 2000, dennis roberts wrote:
At 04:00 PM 4/7/00 -0500, Michael Granaas wrote:
But whatever form hypothesis testing takes it must first and formost be
viewed in the context of the question being asked.
this seems to be the key to REinventing ourselves ... make sure the
Bruce Weaver wrote (in part):
...Negative priming is measured as a response time
difference between 2 conditions in an experiment. The difference is
typically between about 20 and 40 milliseconds...
The researcher KNOWS that a lot of other things affect
response
the term 'null' does NOT mean 0 (zero) ... though it is misconstrued that way
the term 'null' means a hypothesis that is the straw dog case ... for which
we are hoping that sample data will allow us to NULLIFY ...
in some cases, the null happens to be 0 ... but in many cases, it does not
Dennis Roberts wrote:
the term 'null' does NOT mean 0 (zero) ... though it is misconstrued that
way
the term 'null' means a hypothesis that is the straw dog case ... for
which
we are hoping that sample data will allow us to NULLIFY ...
in some cases, the null happens to be 0 ... but in many
On Mon, 10 Apr 2000, Robert Dawson wrote:
Dennis Roberts wrote:
the term 'null' does NOT mean 0 (zero) ... though it is misconstrued that
way
the term 'null' means a hypothesis that is the straw dog case ... for
which
we are hoping that sample data will allow us to NULLIFY ...
in
Dennis Roberts wrote:
if you are interested in the relationship between heights and weights of
people, in the larger population ... the notion that we test this against
a
null of rho=0 is not credible ... in fact, it is rather stupid ... a more
sensible null would be perhaps a rho of .5 ...
Michael Granaas wrote:
My grandmother could have told me that the mean height for men and women
was not the same (zero difference). So based on prior evidence I
hypothesize that the actual difference is 3 inches (mu1 - mu2 = 3) and use
that for my null hypothesis. True, I can reduce this
I am new to the list so I am jumping into the middle of this. However, we
have to start teaching hypothesis testing somewhere. Even if it goes the
way of the Edsel, it will be a slow death because many of us will continue
to use when we feel it is appropriate to the question. However, I tell my
At 01:16 PM 4/10/00 -0300, Robert Dawson wrote:
No if you have to start "a more sensible null would be perhaps" you
almost surely do not have a hypothesis worth testing.
now we get to the crux of the matter ... WHY do we need a null ... or any
hypothesis ... (credible and/or
At 01:16 PM 4/10/00 -0300, Robert Dawson wrote:
both leave the listener wondering "why 0.5?" If the only answer is "well,
it was a round number close enough to x bar [or "to my guesstimate before
the experiment"] not to seem silly, but far enough away that I thought I
could reject it." then the
I, and I think Dennis, are arguing that when we test a hypothesis we
should have a null hypothesis that is plausibly true. A hypothesis that
reflects some sort of an effect size estimate where such an estimate is
meaningful.
If I understand correctly Robert is arguing that we should always
On Mon, 10 Apr 2000, Robert Dawson wrote:
Michael Granaas wrote:
H0: being in the target population has no effect on sexual dimorphism in
height
Ha: being in the target population does affect sexual dimorphism in
height
I want to see if I am interpreting your meaning correctly.
On Mon, 10 Apr 2000, dennis roberts wrote in part:
.. the fact that we create a null and test a null does NOT imply that
we are therefore testing some effect size ...
Of course not. One does not TEST an effect size, one ESTIMATES it.
And it is useful to do so only if one has found it not
The free ARC software from the University of Minnesota will do some of this.
Look at
http://stat.umn.edu/ARCHIVES/archives.html
Jon Cryer
At 01:59 PM 4/10/00 -0500, you wrote:
Hello all,
I'm looking for software that can display a 3-D regression environment (x,
y, and z variables) and draw a
My comments are about half-way down.
Michael
On Mon, 10 Apr 2000, Robert Dawson wrote:
Dennis Roberts wrote:
now we get to the crux of the matter ... WHY do we need a null ... or any
hypothesis ... (credible and/or sensible) to test??? what is the
scientific
need for this? what is
here are a few (fastly found i admit) urls about scientific method ... some
are quite interesting
http://dharma-haven.org/science/myth-of-scientific-method.htm#Overview
http://teacher.nsrl.rochester.edu/phy_labs/AppendixE/AppendixE.html
http://idt.net/~nelsonb/bridgman.html
Just because Dennis has trouble with the null hypothesis, that does
not mean that it is a bad idea to use them.
On 10 Apr 2000 08:41:06 -0700, [EMAIL PROTECTED] (dennis roberts)
wrote:
the term 'null' does NOT mean 0 (zero) ... though it is misconstrued that way
the term 'null' means a
the logic behind the null hypothesis method is flawed ... IF you are
looking for truth AND you keep following the logic of testing AGAINST a
null ...
first, say you reject the null of rho = 0 ...
then, LOGICALLY ... this says that since we don't know what truth is ...
just what we think it
- Original Message -
From: Michael Granaas [EMAIL PROTECTED]
Our current verbal lables leave much to be
desired. Depending on who you ask the "null hypothesis"
is a) a hypothesis of no effect (nil hypothesis) b) an
a priori false hypothesis to be rejected (straw dog hypothesis)
Can anyone help with good resources on the web, journals, books, etc on
cluster analysis - simularity and ordination. Any recommended programs
for this type of analysis too.
Cheers
Elisa Wood
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