Confidence interval for measure that lies within [0, 1] I have some metrics that all lie within [0,1], and I have multiple measurements of each. For example, one measure is accuracy (for a machine learning application). Accuracy will always lie within [0,1], and given multiple rounds of N-fold cross validation, there will be multiple measurements of accuracy. So let's say I have 50 values of accuracy, how can I form a confidence interval around accuracy, such that the confidence interval does not extend above 1 or below 0? More generally, how can I compute a confidence interval around an average of multiple measurements of a variable that always lies in [0,1], but does not necessarily adhere to a uniform distribution (e.g., a [0,1] truncated normal distribution)?

More of a comment than an answer, but I need the space provided by Answer format.
If you have 50 values from an unknown distribution with support $[0,1],$
then you might consider a bootstrap confidence interval for the population mean.
# Fictitious data 
set.seed(714)
x = rbeta(50, 4, 10)   # Pop. mean 4/14 = 0.2857
summary(x)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1026  0.2131  0.2925  0.2948  0.3440  0.5638 

# Quantile bootstrap
set.seed(2021)
a.re = replicate(3000, mean(sample(x, 50, rep=T)))
CI = quantile(a.re, c(.025,.975));  CI

     2.5%     97.5% 
0.2636896 0.3252635   # contains pop. mean (this time & usually)

hist(a.re, prob=T, col="skyblue2", main="Bootrap Dist'n: Resampled Means")
 abline(v=CI, col="orange", lwd=2, lty="dotted")


There are many styles of 95% nonparametric bootstrap CIs, just be sure to choose
one that is constrained to give endpoints in $[0,1].$
