I have a relatively small language corpus (ca. 250000 tokens). There are two possible stems for a verb in the language: active and passive. Some of the verbs in the language occur only in passive stems (like deponent verbs in Latin, for example). There is also a number of verbs, of which there are only a few instances in the corpus. These instances happen to display passive forms. I would like to evaluate these passive-only verbs and to select real deponents out of them (e.g. if a verb has 0 active forms and 16 passive, then it is more likely to be a deponent, contrary to the verb which has, for example, 0 active forms and 2 passive).
What I have already done: 1) I gathered a small number of verbs which mostly have passive forms (more than 75%). Selecting verbs where the forms are 50-50 would lead to a lot of false positive results. The data looks like this:
VERB N_ACTIVE N_PASSIVE PERCENTAGE_PASSIVE Verb_1 3 16 0.84 Verb_2 2 20 0.9 ....
2) Then I took the weighted average of this group. It turned out to be approximately 92%. 3) Then I assumed that the occurences of a verb were independent of each other, so having met n passive forms for a verb in my corpus I can assume that the probability of this is 0.92 ^ n.
The problem: I am not sure how I would test the significance of the difference between 0.92 ^ n for a given verb and the obtained average. I tried applying t-test, but it provides weird results, and I think that it is not best suited for this kind of study. I would be grateful if anyone could point the direction in which I should be thinking/reading.