diff --git a/Cargo.toml b/Cargo.toml
index d823ef0..68eb873 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -18,6 +18,10 @@ name = "mean"
 harness = false
 name = "min"
 
+[[bench]]
+harness = false
+name = "kurtosis"
+
 [dependencies]
 num-traits = "0.1"
 num-integer = "0.1"
diff --git a/benches/kurtosis.rs b/benches/kurtosis.rs
new file mode 100644
index 0000000..0dfb3e5
--- /dev/null
+++ b/benches/kurtosis.rs
@@ -0,0 +1,41 @@
+#![cfg_attr(feature = "cargo-clippy", allow(float_cmp, map_clone))]
+
+#[macro_use] extern crate bencher;
+extern crate rand;
+
+extern crate average;
+
+use bencher::Bencher;
+
+/// Create a random vector by sampling from a normal distribution.
+fn initialize_vec() -> Vec<f64> {
+    use rand::distributions::{Normal, IndependentSample};
+    use rand::{XorShiftRng, SeedableRng};
+    let normal = Normal::new(2.0, 3.0);
+    let n = 1_000_000;
+    let mut values = Vec::with_capacity(n);
+    let mut rng = XorShiftRng::from_seed([1, 2, 3, 4]);
+    for _ in 0..n {
+        values.push(normal.ind_sample(&mut rng));
+    }
+    values
+}
+
+fn bench_kurtosis(b: &mut Bencher) {
+    let values = initialize_vec();
+    b.iter(|| {
+        let m: average::Kurtosis = values.iter().map(|x| *x).collect();
+        m
+    });
+}
+
+fn bench_moments(b: &mut Bencher) {
+    let values = initialize_vec();
+    b.iter(|| {
+        let m: average::Moments = values.iter().map(|x| *x).collect();
+        m
+    });
+}
+
+benchmark_group!(benches, bench_kurtosis, bench_moments);
+benchmark_main!(benches);
diff --git a/src/lib.rs b/src/lib.rs
index e348cff..93191c9 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -74,8 +74,7 @@
 
 #![cfg_attr(feature = "cargo-clippy", allow(float_cmp))]
 
-//#![no_std]
-extern crate core;
+#![no_std]
 
 extern crate conv;
 extern crate quickersort;
diff --git a/src/moments/kurtosis.rs b/src/moments/kurtosis.rs
index 9cb1a24..acdc9d0 100644
--- a/src/moments/kurtosis.rs
+++ b/src/moments/kurtosis.rs
@@ -46,7 +46,6 @@ impl Kurtosis {
             + 6. * delta_n_sq * self.avg.avg.sum_2
             - 4. * delta_n * self.avg.sum_3;
         self.avg.add_inner(delta, delta_n);
-        println!("skewness={} kurtosis={}", self.skewness(), self.kurtosis());
     }
 
     /// Determine whether the sample is empty.