Hi,
I was just going to send this when I saw Erik's post. He's right -- we
can't say anything about your data, but we can say something about
using a t-test.
I'm not a "real" statistician, so this answer isn't very rigorous, but
might be helpful.
On Sep 16, 2009, at 2:55 PM, Robert Hall wrote:
I believe the t-test checks for difference amongst the two sets, and
p-value
< 0.05 means both thesets are statistically different.
A t-test is used to check if the difference in the mean of two samples
is statistically significant.
The null hypothesis is that the two means are not different.
If you reject the null, it means you have reason to believe that the
means of the two samples are different.
See the uses section here:
http://en.wikipedia.org/wiki/Student's_t-test
Here while checking
for dissimilarity the p-value is 0.3288, does it mean that higher the
p-value (while t.test checks for dis-similarity) means more similar
the
results are (which is the case above as the means of the results are
very
close!)
Please help me interpret the results..
Your intuition is essentially correct. In general, the higher the p-
value (in any statistical test), the less confident you should be that
rejecting the null hypothesis is a good idea.
-steve
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
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