Expressing Confidence Intervals as a percentage for simplicity I am running some stats on a feeding experiment where 6 rodents were given the choice between control seeds and treated seeds for 12 hours. seed count consumed was the response variable. I've analyzed the data using a paired t-test and got a p-value of 0.0007. I'm assuming this is the right analysis to do but let me know if you disagree. Here is the raw data.

Mean control seed consumed 1082
Mean treated seed consumed 32.5
For the sake of making the data easy for the layperson to interpret, I would like to present the results as a percentage which I got using the following equation which I applied to the means 
control seeds consumed / total seeds consumed 
1082/(1082+32.5)= 96.8%
I noticed that if I apply this same equation to the 6 replicates individually I get 
100%
100%
92.9%
99.4%
99.8%
100%
which if averaged yields 98.1%
So which is correct 98.1% or 96.8%
Should I be taking the average of the proportions or the proportion of the averages
In the end I would like to be able to say "rodents showed a (##)% +/- (#)% preference for untreated seeds" 
I'm also not sure how to do the +/-. should this plus or minus be the standard error? Should I calculate it from the equation... 
σp = sqrt [ P(1 - P) / n ]
using the proportion of the averages 96.8%
or use the equation
σx = σ / sqrt( n )
using the list of proportions to find the standard deviation and applying the standard error to the average of the proportion 98.1%
 A: You have seen the behavior of only six mice. Four avoided eating treated seeds altogether and two showed a substantially reduced appetite for treated seeds. There are many more than six mice of various kinds in the universe, so it is a stretch to say the treatment deters most of them. Nevertheless, the data you have give an early indication that
the treatment may be promising.
The difference ctrl - trt passes a Shapiro-Wilk test for normality. It seems OK to look at results of a paired test, which (not surprisingly) strongly rejects the null hypothesis that that there is no significant difference in preferences of half a dozen mice. They prefer eating untreated seeds.  Because one can't 'interview' mice, asking what they
think of treated seeds, you can take their eating behavior as a proxy
for "Treated seeds are yucky."
Your measure of 'proportion of control seeds consumed' might be OK if half of
several thousand randomly sampled seeds were treated and half not, and then seeds were deployed to thousands of independent places. Then there would be a basis for statements about the fortunes of treated vs. untreated seeds. 
In your experiment, however, mice were the experimental units. You have
limited information on the behavior of mice. In summary, you might say
something like what I did at the start:

"Four of six mice avoided eating treated seeds altogether and remaining two showed a substantially reduced appetite for treated seeds."

A: First, before you start generalizing (drawing inferences) from six mice, you should establish what population of mice these six are a random sample of.  If you can't do that, then I'd go with what @BruceET said. 
Second, these results pass the IOTT - the interocular trauma test - it hits you between the eyes. It seems like overkill to do all the work you are doing.
Third, your two numbers are answers to different questions. The first question is "Of all seeds eaten, what proportion were control and what proportion were treated?" and the second is "What is the average proportion of control seeds eaten?"  They get different answers because different rats ate different numbers of seeds.
Finally, for the standard error, the usual formula is $SE = \sqrt{\frac{p(1-p)}{n}}$. This isn't perfect and a bunch of alternatives have been proposed, but, given how extremely clear the results are, this estimate is probably fine for your purposes. 
