With a small sample size I can see limited value of controls - especially if I had age and strain and am expecting exceptional results. I would prefer to use the resource to produce a larger sample of the experimental group or groups.
I have to agree with Turnbuckle in principle against considering this an n=2 study if any statistics about the group are going to be discussed. Although given that with n=3 and no controls, any statistics would have very little power to them anyway. I just think it would make us look bad tossing a non-favorable data point after the fact. Detracting from our general credibility.
The concept of after the fact data selection has its uses. I found it discussed in this interesting paper on
Retrospective Power Analyses which mentions:
The importance of doing a power analysis before beginning a study (prospective power analysis) is universally accepted: such analyses help us to decide how many samples are required to have a good chance of getting unambiguous results. In contrast, the role of power analysis after the data are collected and analyzed (retrospective power analysis) is controversial, as is evidenced by the papers of Reed and Blaustein (1995) and Hayes and Steidl (1997). The controversy is over the use of information from the sample data in retrospective power calculations. As I will show, the type of information used has fundamental implications for the value of such analyses. I compare the approaches to calculating retrospective power, noting the strengths and weaknesses of each, and make general recommendations as to how and when retrospective power analyses should be conducted.
The problem here as I see it is that it could look like we're cherry picking our data to reach arbitrary conclusions.
But I could see the usefulness of tabulating subjects that die of cancer/tumors compared to those that don't. But you'd need some reference points to draw any conclusions. Without controls or at least a breed with known statistics, I don't see how that could be meaningful.
Howard