diff --git a/src/error_analysis.rs b/src/error_analysis.rs
index 6f04541..1cce049 100644
--- a/src/error_analysis.rs
+++ b/src/error_analysis.rs
@@ -32,7 +32,7 @@ fn generate_random_weighted_dataset(ds: Dataset, rng: &mut StdRng) -> Dataset {
 // 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>) {
+pub fn run_bootstrap(cfg: &Config, ds: Dataset, num_runs: usize) -> (Vec<f64>,Vec<f64>) {
     // seed the rng
     let mut rng: StdRng = SeedableRng::seed_from_u64(cfg.bootstrap_seed);
 
diff --git a/src/lib.rs b/src/lib.rs
index 36179d8..90ec06f 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -189,7 +189,7 @@ pub fn run(cfg: &Config) -> Result<()>{
 
 	let (P_std, free_energy_std) = if cfg.bootstrap > 0 {
         println!("Bootstrapping..");      
-		error_analysis::run_bootstrap(&cfg, dataset.clone(), &P, cfg.bootstrap)
+		error_analysis::run_bootstrap(&cfg, dataset.clone(), cfg.bootstrap)
 	} else {
 		(vec![0.0; P.len()], vec![0.0; P.len()])
 	};