Dear all,

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
```

I get

```
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!