Hi there!
I have two little different data. One is a computer test on people, the
other is a paper and pencil test. two boxplots show me that the data is
almost the same.
So now I'd like to know if I could handle all data as one, by testing
with ks.test:
====
ks.test(el$angststoer, fl$angststoer)
Two-sample Kolmogorov-Smirnov test
data: el$angststoer and fl$angststoer
D = 0.1413, p-value = 0.9112
alternative hypothesis: two-sided
Warning message:
In ks.test(el$angststoer, fl$angststoer) :
cannot compute correct p-values with ties
====
Ok, so how can I get the p-value?
I tried two tests:
====
ks.test(fl$angststoer, "dnorm")
One-sample Kolmogorov-Smirnov test
data: fl$angststoer
D = 0.8109, p-value < 2.2e-16
alternative hypothesis: two-sided
Warning message:
In ks.test(fl$angststoer, "dnorm") :
cannot compute correct p-values with ties
====
so I see that this message in the first one, depends on fl$angststoer.
The I have on this two vectors:
====
[1] -2.2407100 -2.8601209 -0.5005659 -2.4007721 -0.3474336 -2.6653452
[7] 0.6548865 -1.6281751 -1.2940679 -0.1316566 -1.4541612 -1.6560206
[13] -0.7441850 0.8219399 0.1746081 -1.2314248 -3.8910969 0.1328448
[19] -1.8439508 -0.8833972 -0.4936052 -0.1664593 -0.8694749 -2.8253588
====
Doesn't seem to be a problem?
What can I do for a good computation?
Thanks,
Martin