Re: Doesn't a t-test work here?

jgpowers@xxxxxxxxx wrote:
On Feb 22, 11:51 am, jgpow...@xxxxxxxxx wrote:
Okay I have done a bit of reading. Based on what I have read I still
feel like T-tests would be alright. Perhaps it would be more powerful
to run an ANOVA followed by a Dunnett's but I think for my purposes a
t-test would be okay.
It's not a question of power (in the statistical sense). It's a question of not finding differences when there are none.

Here are a couple websites that I think support
my thoughts:

http://www.anselm.edu/homepage/jpitocch/biostats/keysmeans.html
Based on this webpage I think I need a 2 independent sample t-test 1 -
direction
Are we looking at the same web page? :-)

Number of means: 3 or more
Comparisons: 3 or more means being compared after a significant ANOVA
Type of Comparisons: Any differences between each experimental group mean and the control group mean

This describes your situation exactly, and recommends Dunnett's test.

http://www.aiaccess.net/e_t.htm
"3) The third form of the t-test, called the "Two Independent Samples
t-test"
This web page covers material that would be taught in a first course to undergraduates with no mathematical background. It isn't designed to cover messier situations.


I think where everyone gets tripped up is the fact that I have like 9
different treatments all being compared to a control treatment. But
those 9 treatments are all completely different and essentially
unrelated.
THey're not. You are analyzing them in the same paper.
For all intents and purposes I am really only looking at
two sets of data at any given time, one set of treated leaves vs. the
set of control treatment leaves.

I know you guys are all statistics people so you will probably hate me
when I say that based on my reading an ANOVA/Dunnett would be
statistically more powerful, but being so late in the game with this
paper I am hesitant to change my statistical analysis now.
If you are going to do that, you should be careful to state that your significance level is per-treatment. Even then, the referee would be within their rights to insist on Dunnett's or something similar.

Regarding plant selection and randomizing these things I do realize
that there is a level of inherent uncertainty when doing these sorts
of statistical comparisons, and I also realize that it is not ideal to
ignore plant variations, but I think for my purposes it should be
okay. These plants are not like humans, they are all clones of each
other. All grown from the same batch of seeds. All planted in the
same soil, watered at the same time, exposed to the same amount of
light, etc. etc. I realize there will always some differences, even
in clones, but these should be minor.

You haven't convinced me that the plants are identical genetically, but I feel a little better. You can check this - do an ANOVA on the pairwise sums and the pairwise differences, and compare the error terms.
.

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