Can anyone explain this madness to me?
I am performing a t-test using the t.test function with default parameters.
My two data arrays are this
control test 2.75E-05 0.000395 3.7E-05 0.000429 1.78E-05 2.51E-05 2.05E-05 2.19E-05 2.52E-05 3.15E-05 2.21E-05
Here is the result:
Welch Two Sample t-test data: control and test t = -22.589, df = 1.0272, p-value = 0.02602 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.0005909187 -0.0001822813 sample estimates: mean of x mean of y 2.54e-05 4.12e-04
How is this possible to get such a high p-value with such data?
By contrast, if I use this dataset:
control test 0.000187 0.000346 7.41E-05 0.000368 4.27E-05 0.000125 4.92E-05 0.000114 6.56E-05 9.44E-05 7.52E-05
Welch Two Sample t-test data: as.numeric(mmm) and as.numeric(lll) t = 14.261, df = 5.7007, p-value = 1.123e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.0002190199 0.0003111578 sample estimates: mean of x mean of y 3.570000e-04 9.191111e-05
It looks like sorcery to me. The first dataset ought to give a much smaller p-value than the upper one.
Moreover, when I do the t-test in libreoffice, I get the expected result, i.e. p=2.3E-12 for the first dataset and p=2.35E-05 for the second.
What is wrong with my test?
thanks for any input!