I am working on a project where I am evaluating software packages. I have got the results of two experiments where only one variable has been changed. Each experiment has been conducted 10 times. I am doing paired-sample t-tests. For some of my metrics, the results are exactly the same for the ten iterations. Therefore I have a standard error of zero and I cannot do the t-test.
If I am correct you can use the p-value generated during a t-test to support your claim that the different values of the variables had an impact on the results (as you can say that your results are statically significant). In my case, what can I say/do to support such claim?
EDIT: From the comment, I am going to add details on my design.
I'm am evaluating file carvers, these are digital forensics tools which can be used to extract files from forensics images (such as hard drives). I've planted a known number of evidence (500) and configured each file carver to look for these evidence. I have got tow set of evidence. One is a "well known" evidence (that I created myself for the purpose of the research, so I am planting 500 time the same file) and a realistic set of evidence (composed of 500 unique files). I am doing two tests with the same tool: one for each set of planted evidence. If I understood correctly the different type of t-tests, I can do a paired t-test?
From the results that I have got, the tools fail each time to recover all evidence. However, the recovered evidence are the same each time. The tools are working as a search function. Such function might fail if it is run hundreds of times, maybe I need much more. I have tested four tools with different scenarios and the results never varied (same true positives, false positives and false negatives)
What I need, is a way to support my findings. When you look at the literature you get told how to talk about p-values results. I just have no idea what I should say if it cannot be computed as their is no variation in my results. I found some articles where the researchers had similar results but they were just ignored (only displayed in the table). It might be obvious but as it is the first time that I'm using stats in my work I'm a bit lost (and I'm self-taught so I might have poorly understood some concepts)
Finally, my main question is: The results show clearly that the type of evidence as an impact on the results. How can I support such claim in a scientific fashion?