On Feb 22, 11:51 am, jgpow...@[EMAIL PROTECTED]
wrote:
> On Feb 22, 11:14 am, Allen McIntosh <nos...@[EMAIL PROTECTED]
> wrote:
>
> > z wrote:
> > > what they're getting act, i think, is that if a t-test is designed
to
> > > be significant or not at the .95 level, that means that 1 time out
of
> > > 20 it will show a significant difference when there is really no
> > > difference between the treatments, out of random "noise". this is
the
> > > generally accepted level of error. however, what you are doing is
> > > basically 10 t-tests simultaneously, so your chance of finding a
> > > significant difference somewhere when in fact there are none at all
is
> > > 1-(.95)^10, or about .4, if you treat each t-test as though it were
> > > alone and require a .95 confidence limit. this kind of error rate is
> > > obviously a bit high.
>
> > The 10 tests aren't independent, so you can't calculate the chance of
> > finding a significant difference that way.
>
> 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. Here are a couple websites that I think sup****t
> 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
>
> http://www.aiaccess.net/e_t.htm
> "3) The third form of the t-test, called the "Two Independent Samples
> t-test", looks very similar to the previous one. We still have two
> sets of measurements, and we are trying to figure out if the averages
> of these two sets of measurements are significantly different. But
> this time, we assume that there is no relation****p whatsoever between
> the two sets of measurements, because they were conducted on two, non
> intersecting sets of individuals."
>
> 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. 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.
Allen and others,
Thanks for your help. I did indeed talk to a real live statistician
(sort of) when I was first doing the experiment. He was a doctoral
student who just happened to have to work in the Statistics department
help center when I was analyzing my data. While he didn't help in
experimental design, he did help me do the data analysis. He
essentially said t-tests were fine but that it would be more
statistically powerful to do the ANOVA. I don't recall him saying
ANOVA followed by Dunnett's, but he very well may have, this was
awhile back (for all I know maybe this was understood). Unfortunately
now that I have reviewers comments, he is not responding to my emails
for guidance. Beyond this (and this is where I am really screwed) I
am actually in Korea for the next 4 months doing a research-based
study abroad program. Argh! So going back to a Statistics help
center is also not possible. With that said, don't worry I am not
relying solely on this messageboard for guidance, I found it by
accident, while doing some Googling on my own, but the advice thus far
has been very useful and I greatly appreciate it. Perhaps I will
wander over to one of the Biology groups after this to try to help
some other poor lost soul.
Allen: I made my 11:51 post before I read your 11:41 post. To answer
your question there are 10 data points for each set of treatments.
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.
Perhaps one final rephrasing of the problem:
10 different leaves were treated with a Sample X. 10 other leaves
were treated with a Control. After a period of time I measured GFP
fluorescence from the leaves. I want to compare whether the
measurements of GFP fluorescence are statistically different between
Sample X and the Control.
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