How to define sampling unit. t.test vs proportion test In my particular case I am comparing damaged fruit in several treatment fields vs. several control fields
For simplicity:
Both treatment and control have 10 bushes each. a cluster of fruit is taken from each bush (clusters vary in size). The proportion of damaged fruit for each cluster is noted. (I do have access to the original # of fruit per cluster)
For each bush I have the proportion of damaged fruit.  
When I perform a test I would like to compare
H0: "Proportion damaged fruit in" treatment <= Proportion damaged fruit in 
control
Ha: "" treatment > "" control 

(treatment is a lure which is supposed to attract damage)
I performed a t.test(propotion.damaged ~ treatment)
because I am comparing mean(proportion.damaged in treatment) vs mean(proportion damaged in control)
However, it occurs to me that 
mean(proportion.damaged) = 1/n*sum(proportion.damaged) = (total damaged fruit)/(all the fruit)

Does this mean I should perform a proportions test?
prop.test((damaged fruit treatment/total fruit treatment) vs. (damaged fruit control/total fruit control)

The difference here would be that my sampling unit in the t.test scenario is a bush. Whereas in the proportions test scenario my sampling unit would be a fruit.
Thank you very much for your help.
 A: I think you have framed the question correctly.  How you approach the analysis depends upon if you consider the unit of observation as a cluster or as an individual fruit.  That is, if you are considering each fruit a countable unit or not.
Usually in horticulture, measurements like "percent diseased" or "percent control" are treated as an inherent proportion or percentage.  That is, there is not necessarily a countable unit underlying the percentage.  Imagine a turfgrass plot.  You might say it has 50% dollar spot inspection --- and you might have some tools to make this measurement more precise --- but there's not a countable area that you can treat as an observation unit.
Likewise, in your case it might make sense to treat each cluster or each bush as the experimental unit.
My inclination in this case would be to not use each fruit as an experimental unit. To me, that wouldn't match up with how people or pests interact with clusters of fruit. 
A: Should use mixed effect logistic regression.
Data format:
         Cluster  damaged  count treatment 
            1         1      2       1
            1         0      98      1
            2         1      15      1
            2         0      75      1
           ....

damaged = 1 means damaged and = 0 means not damaged. count = # of fruits. Treatment is code for two treatment (treatment and control).
The model would be
$$\log\left (\frac {\Pr(Y_{ij}=1)}{\Pr(Y_{ij}=0)} \right) = \beta_0 +\beta_1 X_{i} + \gamma_i$$ 
Where $Y_{ij}$ = 1 if the j-th fruit in i-th cluster is damaged, = 0 otherwise. 
$X_i$ is the treatment received in i-th cluster. $\gamma_i \sim N(0,\sigma^2)$ is rnadom intercept for cluster $i$. 
In the program, the information in column count should be used. the common statements are: weight, count, freq, ...
