Timeline for Compare two distributions of large sizes and unequal variances where one distribution is heavily skewed
Current License: CC BY-SA 3.0
7 events
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| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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| Dec 6, 2014 at 5:33 | comment | added | joelostblom | I am still working on the stats, but I think I came up with a nice way to plot it. A little busy but I like it | |
| Dec 3, 2014 at 17:29 | comment | added | robin.datadrivers | Also look at the thread I referenced on comparing distributions, there are some good ideas there. | |
| Dec 3, 2014 at 17:26 | comment | added | robin.datadrivers | Calculate an effect size. I've seen people do that resampling thing, and I just don't get it. It feels like you are fishing. You have statistical significance - why keep looking for it? Is the difference substantive enough for you? You could also do a comparison of quantiles if you want - here is an example: freakonometrics.hypotheses.org/4199 | |
| Dec 3, 2014 at 15:30 | comment | added | joelostblom | Regarding the third point, I am interested to say that my two conditions render a different effect on the cells as measured by this one output variable. In my mind, this difference include both differences in summary statistics and in distribution shape. One additional thing I thought of, could I sample say 1000 cells from each of my data sets and then T-test/mannwhitenyu those to get around the problem of having a too large sample? | |
| Dec 3, 2014 at 15:29 | comment | added | joelostblom | Thanks for your reply. Regarding the T-test for non-normality, I have read that it is robust when the sample size is large (e.g. [here] wormbook.org/chapters/www_statisticalanalysis/…). But I read some more on it and maybe a non parametric test is better given the skew/kurtosis of my data. In any case, the p-values I get from mannwhitneyu are similar (even smaller actually, because it is more suitable and gives me more power?) so the large sample problem remains. | |
| Dec 3, 2014 at 5:46 | history | answered | robin.datadrivers | CC BY-SA 3.0 |