Hi everybody,
here is my problem:
I have several regions which are represented by different isotope values and I would like to detect the variability between these regions and the values. Basically, the data looks like
a b c
0.788 0.759 0.797
0.786 0.756 0.798
...
I have performed the Bonett.Seier.test and it gives me a p-value for a,b. So, I assume this test can be used for any distribution and is not strictly tied to normal distribution, right?
Any suggestions what would be (more) useful?
thanks in advance!
Michael
_____
PS: here is the simply code:
#package
library(intervcomp)
#data
data
Article Statistical Inference for a Ratio of Dispersions Using Paired Samples
Jochen Wilhelm attached the correct paper reference to this question, many thanks!
I think, and please correct me if I am wrong, that for small sample sizes, the suggested method could cause problems.
Another solution could probably be:
- a two sample KS-test to evaluate if two populations are drawn from the same distribution..
- ANOVA test for variance to test all groups simultaneously
Estimating variability in small samples is always a extremely problatic, no matter what you do.
yes, that is the crunch... we have samples from 12 micro-regions, each with variable sample size and we need to test whether they are significantly different (or not). If they are different, we could suggest another methodical workflow for the interpretation of our data.
For now, I will perform the ANOVA and a TUKEY HSD test according to this:
data
Your logic is flawed. Being "significantly different" only means that you have sufficient data to demonstrate that the whole of your data is not well explained by a model assuming equal variances. Failing to see a "significant difference" only means that you don't have enough data to make a conclusion. But you take this to conclude that this-or-that workflow is ok. This is not correct. This is the same misconception typically made when testing assumptions for other tests (like normal distribution or homoscedasticity for linear models) and interpreting a "non-significant result" as a confirmation that the assumptions are ok.
I totally agree with you and thanks for pointing this out. I did not explain myself correctly here. Maybe 'significantly different' is the wrong wording and I should say that there is a high probability that the samples are not drawn from the same distribution.
The small sample size is usually what we get in archaeological research that aims at integrating scattered biogeochemical data....
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