On 05/01/2011 17:40, Bert Gunter wrote:
My hypothesis was specified before I did my experiment. Whilst far from
perfect, I've tried to do the best I can to assess rise in resistance,
without going into genetics as it's not possible. (Although may be at the
next institution I've applied for MSc).
With my hypothesis (I mentioned it below), I was of the frame of mind that a
nonsignificant p-value on the cleaner variable (for now - experiment is far
from over), indicated a lack of evidence for rejecting the null. And so at
the minute, it looks like the type of cleaner makes no difference.
I have no fundamental objection, but be careful. I would simply
qualify your last sentence by saying that it means that the
experimental noise is to great to precisely determine the size of the
cleaner effect. Scientific reality tells us that it is never exactly
0; what your results show is that your uncertainty about the value of
the difference encompasses both positive and negative values. This
does NOT mean that the difference might not be scientifically large
enough to be of interest -- a confidence interval for the difference
(MUCH better than a P value) would help you determine that. If the
interval is narrow enough that the difference, positive or negative,
is too small to be of scientific interest, then you're done. If the
linterval is large, then it tells you that you need more data, a
better experiment (less noisy) etc.
-- Bert
At the moment I wouldn't call the confidence interval small, it's
definately wide, and at the minute the confidence interval covers zero.
My R-squared at the minite is also 0.5, this is mostly due to the few
extreme cases of adaptation as I mentioned before, but I'm hesitant to
remove it as papers in my literature study which also evolve bacteria
show that there is often (sometimes wide) variation in the paths
populations take. So whilst mathematically a bit undesirable, and makes
me and the model uncertain, it does fall into place with what is known,
or has been previously shown of the reality of selection. Again if I
include the data from the bacteria dropped from the study, all that
"improves", and uncertainty is reduced.
It may also be worth me mentioning, I am also taking a more traditional
approach (by that I mean a more "Statistics 101" approach, indeed that
is all the stats tuition covered in my course as a taught element),
incase what I've described above did not work or was not ideal, because
we (me and my supervisor) did forsee a model report may contain a lot of
uncertainty. Indeed we did expect some populations to adapt and some to
not etc. So I've also been collecting data on the width of the zones of
inhibition shown by putting disks of cleaner on plates of growth, and
measuring the dead zone that results. I can get lots of data from this
with only a few plates, and doing this at the start of the study, a few
times in the middle, and at the end. Will allow me to do more
traditional analysis, for example t.test on the dead zone widths at the
end of the study, between cleaner a and b. Or a non parametric
equivalent, maybe even a permutation test. The modelling stuff is
already beyond what my supervisor expects of me, but I felt it would add
value and a lot more insight to the study, allowing more variables to be
accounted for, than a more short-sighted traditional "test".
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