In article <[EMAIL PROTECTED]>,
[EMAIL PROTECTED] says...
>
>I am trying to formulate a calculator policy in a department that currently
>allows any calculator except "those capable of storing text". That rules
>out all of the graphing calculators since they have alphanumeric
>capability. I use
Brian E. Smith wrote on 3/23/00 1:01 AM:
>I am trying to formulate a calculator policy in a department that currently
>allows any calculator except "those capable of storing text". That rules
>out all of the graphing calculators since they have alphanumeric
>capability. I use a TI-83 or TI-86 i
I am trying to formulate a calculator policy in a department that currently
allows any calculator except "those capable of storing text". That rules
out all of the graphing calculators since they have alphanumeric
capability. I use a TI-83 or TI-86 in my statistics class but under the
current po
Hi, Carl ---
If you still have your copy of
Introduction to Linear Models (Ward &
Jennings)
you will find many examples in Chapters 10 and
11.
An interesting example is on paged 217,
11.9 Discontinuity Between Two
Second-Degree Polynomials.
With facility to create linear models
appr
[EMAIL PROTECTED] wrote:
: had forgotten what a p-value is. I find it helpful to explain
: significance tests and the outcome of a study
: as follows:
:
: The difference is either
:
: Statistically significant and big enough to be of practical importance.
: Statistically not significant and
Robert McGrath ([EMAIL PROTECTED]) wrote:
:
: If what you mean is that really large samples can lead to distorted
: results of significance tests, I would disagree. The problem is not
: that the sample is too big, but that Significance tests are interpreted in
: inappropritae ways when readers as
I am really puzzled at this idea that a test can be 'too significant'.
All that a test does is to give a measure of the weight of the statistical
evidence provided by the data - within the scope of the models used. Something
like: 'It is highly likely that the alternative model is preferable to t
Carl...even though brief, Pedhazur and Schmelkin (1991). Measurement,
design, and analysis. Hillsdale, NJ: Lawrence Erlbaum. do a nice job
discussing this subjecton pages 296-301...as well as Cook & Campbell
(1979). Quasi-experimentation. Hougton Mifflin (pp.137-146 and
202-205)...h
- I agree with a couple of others, DeLa presents a bad idea. The
price being the same, MORE information is BETTER -
On Wed, 22 Mar 2000 13:59:19 GMT, "DeLa" <[EMAIL PROTECTED]>
wrote:
> Well, I suppose that when the sample is too big almost every
> relation will prove to be "significant". A lo
Five New Jersey Area announcements:
===( Announcement #1: Short Course )===
The New Jersey and New York City Metro Chapters Present:
An American Statistical Association Short Course,
An Introduction to Logistic Regression
Stanley Lemeshow, Ph.D.
On Wed, 22 Mar 2000, DeLa wrote:
> I have been trying to explain to some co-workers that a sample
> can be too big.
> That is not very easy because ...
... as everyone knows, more is better.
> [... and] because it is contradictory to what
> intuition says.
Only untutored intuit
On Wed, 22 Mar 2000, dennis roberts wrote in part:
> larger sample sizes (random i hope) get us closer to the answer we want
> ... what is the parameter or ... what is it NOT
>
> in this context, you can't have a sample that is too large since, it
> will systematically get you closer and close
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folks, i am copying to the edstat list this message from a fellow who
asks for help for what appears to be a worthy cause. i have edited
somewhat for reading ease.
he is looking for advice on designing a survey in re
"Hence, to monitor the progress of the
project a it has been decided that a ba
When we focus on estimates of effect sizes and the stability of those
estimates, we are delighted to have a huge sample. Don't focus on
statistical significance.
===
This list is open to everyone. Occasionally, less thou
On Mon, 20 Mar 2000 09:28:58 -0500, Taweewan Sidthidet
<[EMAIL PROTECTED]> wrote:
> Can anyone give me the answer this question:
> Will the confidence interval for an unconditional forecast be wider or
> narrower if the exogenous variable has greater variation in sample? why?
"greater variation
Cambridge University student films review - visit www.worldreviews.com
===
This list is open to everyone. Occasionally, less thoughtful
people send inappropriate messages. Please DO NOT COMPLAIN TO
THE POSTMASTER about th
Will someone give me a (readable) reference for "regression
discontinuity"? Thanks in advance.
Carl Huberty
===
This list is open to everyone. Occasionally, less thoughtful
people send inappropriate messages. Please DO
DeLa wrote:
> Well, I suppose that when the sample is too big almost every
> relation will prove to be "significant". A lot of
> pseudo-relations will occur. It will become difficult to detect
> intermediate(1) variables or neutralise them because there will
> be many candidates - if not all the
When can a sample be too large?
As an experimental psychologist, I explain to my students that in an
experiment in which you expect your treatments to modify your sample
means, there are always three reasons why one can get a significant
result of a statistical test. (i.e.. a result in the a
MsgTo.com Spam Protection TestI recently posted a message to Edstat-L
and received, shortly afterwards, a message asking me to click on a word to
prove that I was human, so that my message might be forwarded to the
subscriber [EMAIL PROTECTED]
Without thinking, I did so.
However, a moment
>I have been trying to explain to some co-workers
>that a sample can be too big. That is not very
>easy because it is contradictory to what intuition
>says.
>
>Can someone point me to some good arguments or
>literature? Or correct me if my assumption is
>wrong?
Suppose you are taking a random sam
I recommend MINITAB.
I have also had good use from SigmaStat with SigmaPlot for plotting.
In article ,
"news.cwcom.net" <[EMAIL PROTECTED]> wrote:
> > > I am looking for a stastical software package I have only used the
JMP
> > > software (mostly for Design of
One reason why a large sample is not necessarily preferable to a small
sample is
that sampling error is not the sole source of errors in a survey. For
example, it may be better to
conduct a small survey and use resources to reduce non-response than to
conduct a
larger survey and ignore n
Well, I suppose that when the sample is too big almost every
relation will prove to be "significant". A lot of
pseudo-relations will occur. It will become difficult to detect
intermediate(1) variables or neutralise them because there will
be many candidates - if not all the variables will be
inte
- Original Message -
From: DeLa <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Wednesday, March 22, 2000 6:27 AM
Subject: Sample size: way tooo big?
> I have been trying to explain to some co-workers that a sample
> can be too big.
> That is not very easy because it is contratictory
If what you mean is that really large samples can lead to distorted
results of significance tests, I would disagree. The problem is not
that the sample is too big, but that Significance tests are interpreted in
inappropritae ways when readers assume statistical significance equals
clinical signif
1. If you use a sample larger than necessary, the costs for collecting,
processing and analyzing the data are unnecessarily increased.
2. If you take a mechanical approach to statistics, such as looking only at
"statistical significance" and not at practical significance, then you may end
up bei
the purpose and any inferential statistical procedure is to either answer
the question: what is the parameter, or ... to test some specific
hypothesis ABOUT a parameter ...
thus, the goal of inferential statistics IS finding the parameter.
now, significance is nothing more than asking what is
DeLa wrote:
> I have been trying to explain to some co-workers that a sample
> can be too big.
> That is not very easy because it is contratictory to what
> intuition says.
>
> Can someone point me to some good arguments or literature?
> Or correct me if my assumption is wrong?
I can see that hu
In article ,
[EMAIL PROTECTED] says...
> I have been trying to explain to some co-workers that a sample
> can be too big.
> That is not very easy because it is contratictory to what
> intuition says.
>
> Can someone point me to some good arguments or literature?
> Or
I have been trying to explain to some co-workers that a sample
can be too big.
That is not very easy because it is contratictory to what
intuition says.
Can someone point me to some good arguments or literature?
Or correct me if my assumption is wrong?
--DeLa
> Shana Mueller wrote:
> > I am looking for a stastical software package I have only used the JMP
> > software (mostly for Design of Experiments) in the past but am looking
> > for software that will not only help in planning experiments, but also
> > one in which I can input my data from Excel
Dear Statisticians,
Could somebody please give me an advice on WHICH SOFTWARE
CAN CREATE KAPLAN-MEIER SURVIVAL CURVE GRAPHS
WITH ABSOLUTE NUMBERS ADDED UNDER THE X-axis?
(as it is done in this example
http://users.ox.ac.uk/~sg/survival_curve_example.gif
where I added numbers at the bottom manual
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