diff --git a/src/error_analysis.rs b/src/error_analysis.rs
index 7137f37..ee4fdd7 100644
--- a/src/error_analysis.rs
+++ b/src/error_analysis.rs
@@ -4,8 +4,10 @@ use super::perform_wham;
 use super::Config;
 use rgsl::statistics;
 
+// returns a set of num_windows continious weights by
+// a) generate num_windows-1 random variables and sort them
+// b) each weight n is the difference between n+1 and n, where n0=0 and nN+1=1
 fn generate_random_weights(num_windows: usize) -> Vec<f64> {
-    // TODO this is ugly.
     // create a list of num_windows - 1 sorted random numbers and append/prepend 0 and 1
     let mut tmp = (0..num_windows-1).map(|_| rand::random::<f64>()).collect::<Vec<f64>>();
     tmp.sort_by(|a,b| { a.partial_cmp(b).unwrap() });
@@ -21,23 +23,31 @@ fn generate_random_weights(num_windows: usize) -> Vec<f64> {
     return weights
 }
 
-
+// Generate a random weighted dataset from the given dataset by changing the weights
 fn generate_random_weighted_dataset(ds: Dataset) -> Dataset {
     let weights = generate_random_weights(ds.num_windows);
-    Dataset::new_weighted(ds, weights )
+    Dataset::new_weighted(ds, weights)
 }
 
+// Perform bootstrap error analysis. This runs the WHAM analysis num_runs times on random weighted
+// datasets. The standard deviation is calculated on the bootstrapped probabilities of each bin. The
+// standard deviation of the free eneergy is then deduced by error propagation (A_std = kT*1/P*P_std)
 pub fn run_bootstrap(cfg: &Config, ds: Dataset, P: &[f64], num_runs: usize) -> (Vec<f64>,Vec<f64>) {
-    let Ps: Vec<Vec<f64>> = (0..num_runs).map(|x| {
+    // Calculate bootstrapped probabilities
+    let bootstrapped_Ps: Vec<Vec<f64>> = (0..num_runs).map(|x| {
         println!("Bootstrap run {}/{}", x, num_runs);
         let rnd_weighted_dataset = generate_random_weighted_dataset(ds.clone());
         perform_wham(cfg, &rnd_weighted_dataset).unwrap().0
     }).collect();
+
+    // Evaulate standard deviation of P per bin
     let mut P_std = vec![0.0; ds.num_bins];
     for bin in 0..ds.num_bins {
-        let Ps = Ps.iter().map(|window| window[bin]).collect::<Vec<f64>>();
+        let Ps = bootstrapped_Ps.iter().map(|window| window[bin]).collect::<Vec<f64>>();
         P_std[bin] = statistics::sd(&Ps, 1, num_runs);
     }
+
+    // A_std by error propagation
     let A_std = P_std.iter().zip(P.iter()).map(|(std,P)| ds.kT*1.0/P*std).collect();
     (P_std, A_std)
 }