Dear list members,
For those interested on statistical hypothesis testing, null hypothesis
significant testing and p-values I would like to suggest the following
web site with many quotes from many well known statisticians.
http://warnercnr.colostate.edu/~anderson/nester.html
and for new approaches on statistics (moving away from "hypothesis
testing" and p-values) applied to ecology:
Anderson, D. R. 2008. Model based inference in the life sciences.
Springer, NY.
Best,
Manuel
On 01/03/2011 12:46 p.m., Ruchira Datta wrote:
To calculate p-values properly requires paying a lot of attention to how you
choose the null hypothesis and whether it is really appropriate for your
problem and the state of the art. I do not have a lot of experience in
ecology, but in bioinformatics people often choose null hypotheses because
they make the p-values easy to compute, or because everyone does it that
way, or (more cynically) because they make their results appear significant.
One can get a good p-value by choosing a null hypothesis that is almost
certain to be wrong, regardless of the fact that the consensus was already
that this null hypothesis was almost certain to be wrong before any of the
reported experiments were undertaken. That doesn't mean the reported
experiments advanced scientific understanding.
--Ruchira
On Tue, Mar 1, 2011 at 6:24 AM, Jeff Houlahan<jeffh...@unb.ca> wrote:
Hi Chris and all, I actually think that it's a mistake to diminish the role
of p-values. My opinion on this (stongly influenced by the writings of Rob
Peters) is that there is only one way to demonstrate understanding and that
is through prediction. And predictions only demonstrate understanding if
you make better predictions than you would make strictly by chance. The
only way to tell if you've done better than chance is through p-values. So,
while there is a great deal more to science than p-values, the ultimate
tests of whether science has led to increased understanding are p-values.
Best.
Jeff Houlahan
Dept of Biology
100 Tucker Park Road
UNB Saint John
--
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Instituto Internacional en Conservación y Manejo de Vida Silvestre
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