On Fri, 24 Mar 2000 14:13:04 GMT, [EMAIL PROTECTED] (Andy Gilpin)
wrote, citing me ( as ) from 23 March:
snip, some
Oh, Andy, this is such a naive *scaling* conclusion. How can you
regard "power" as a metric that ought to be equal-interval?
Andy
Wait a minute, Rich! I'll admit I'm
It all depends on the least difference of interest
(LDI) in the response.
Sample effects of the order or smaller than the
LDI should be ignored, while larger ones should be
called out. (A sample effect for a variable is the
product of its range times its estimated
coefficient.) In general it is
Hello,
Dale makes much sense, IMHO. Performing a statistical test of a certain
hypothesis is a great place to start, but many leave it at that. Read almost
any clinical journal and you will see the same setup...say means for 2 groups
+/- s.d. (or sometimes s.e.) and then a p-value (N.S. if not
It is possible to have clinical trials which are "over-powered".
At some point, it becomes clinically irrelevant that you are
able to detect a very small improvement in the standard treatment
with high probability. The human resources would be better utilized
in another study which might show a
Now let me jump on Andy!
On Thu, 23 Mar 2000 17:51:04 GMT, [EMAIL PROTECTED] (Andy Gilpin)
wrote:
snip, problem; comment
Still, it seems to me that, other things equal, (a) measuring data
costs a researcher something, and (b) there are clear diminishing
returns in terms of increased
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
In article ye1C4.14$[EMAIL PROTECTED],
[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 correct
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 huge
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
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
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 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
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 intuition.
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
14 matches
Mail list logo
