I have posted last night something about this issue, but now I will reformulate the question.

I have data from animal study in which we had 6 animals in both of 2 groups: control and test. We treated them and I measured amount of enzyme in both of them. I expected that in a test group amount of enzyme is going to be higher than in control group.

1. I did normality testing by GraphPad Prisms ( Kolmogorov-Smirnov test) , and both groups passed.

2. In test group there was one value which was approximately 7x higher than others so I run Grubbs test for outliers. It showed that the 7x higher value is outlier.

3. I excluded outlier, applied one-tailed t-test and I got that there is significant difference between those two groups. But, the mean of test group was Lower than the control group's mean (the opposite than expected).

4.If I keep outlier, run one-tailed t-test there is no significance. But, the mean of test group is in this case HIGHER than the control group's mean.

5. If I apply two-tailed t-test, there is no significance, no matter whether outlier is excluded or not.

I am wondering what would be the best approach: to keep that outlier or not?

And I am wondering if in my case the one-tailed t-test is correct?

I think that the most correct would be to exclude outlier an run two-tailed t-test, but I am not quite sure about that.

I will appreciate any comment, suggestion, advice or discussion. Thanks