My problem should probably be built up from the beginning, so lets start there. I performed a certain experiment 25 times. Every time, the experiment consists of 5000 measurements, and each measurement returns either 1 ('yes') or 0 ('no'). I know that each time I measure 1, the probability that this measurement is correct is $F_1$, while if I measure 0 I know that it's correct with probability $F_0$.
Now, subsequently I want to compute the autocorrelation function of these measurements I use the following formula
with a slightly different normalization, but in principle that should not matter, because what I want to do is fit this autocorrelation to an exponential decay and find the decay time. To do so, I thought I had two options: Either I fit the 25 datasets separately, and then find the mean of the decay time $t_1$ and maybe say something about its variance, or I can find the mean autocorrelation of the 25 datasets, and fit that one. I've decided that that is probably the best option, as the individual datasets can be quite noisy with strange fluctuations, while the mean looks much cleaner.
But now I'm stuck wondering what I should be doing to find the uncertainty in the fit. Should I have introduced some sort of uncertainty due to the imperfect measurements ($F_0$ and $F_1$), or should I have introduced some sort of standard error of the mean for the 'mean autocorrelation' data? That last one seems plausible, as of course there is also a standard deviation in the 25 datasets, but then I get a little confused as to how I should do that. The formula for the standard error of the mean seems to be $\frac{\sigma}{\sqrt{n}}$, but this is a bit unfair as in my 5000 measurements, the autocorrelation drops to 0 after around 50 measurements. For the fit I therefore also only use the first ~100 points of the autocorrelation, as there's no real need to fit the next 4000 points that are all pretty much equal to 0. So should I instead only calculate the mean and standard error of the first 100 points? Would that be a fair way of finding the uncertainty? The subsequent fitting method will already give error bars for the $t_1$, but this of course heavily depends on the uncertainty in the data.
A final sort of sidetrail, what kind of quantitative method of finding how good I fit should I use for exponential decay? Reduced Chi square, Adjusted R squared, or something else entirely? I suppose this is not intended for dsp though, more for a statistics forum, so feel free to ignore it.