Context:
I am teaching a subject and I prepared a multiple-choice quiz for my students.
To have a feeling for which is an acceptable grade I decided to compute a baseline score, which is the score that an agent that answers completely at random would get on the same quiz.
For that, I ran a Monte Carlo simulation and generated N scores for that agent.
Now my goal is to find a value (i.e. a score) that gives me enough confidence/evidence to make me say: "this student did not answer at random". For that, I wanted to compute a 95% confidence interval on these values.
Question:
The question is: in this case does it make more sense to compute the one-sided confidence interval or the two-sided? What each of them does actually tell me? I am not able to really tell which would be the difference for me between these two options.
Also, I realized that if I compute the two-sided confidence interval, the lower boundary is always 0, which means that that 2.5% fraction on the left is all 0s. Does this affect the decision?
I feel like I am not really interested in the lower boundary, but on the other hand if I do a one-left CI, the upper boundary is lower which doesn't really make sense to me.