diff --git a/tests/kurtosis.rs b/tests/kurtosis.rs
index 0632464..7e2c7d7 100644
--- a/tests/kurtosis.rs
+++ b/tests/kurtosis.rs
@@ -2,8 +2,6 @@
 
 extern crate core;
 
-extern crate rand;
-
 use core::iter::Iterator;
 
 use average::Kurtosis;
@@ -57,20 +55,3 @@ fn merge() {
         assert_almost_eq!(avg_total.kurtosis(), avg_left.kurtosis(), 1e-14);
     }
 }
-
-#[test]
-fn exponential_distribution() {
-    use rand::distributions::{Exp, IndependentSample};
-    let lambda = 2.0;
-    let normal = Exp::new(lambda);
-    let mut a = Kurtosis::new();
-    for _ in 0..6_000_000 {
-        a.add(normal.ind_sample(&mut ::rand::thread_rng()));
-    }
-    assert_almost_eq!(a.mean(), 1./lambda, 1e-2);
-    assert_almost_eq!(a.sample_variance().sqrt(), 1./lambda, 1e-2);
-    assert_almost_eq!(a.population_variance().sqrt(), 1./lambda, 1e-2);
-    assert_almost_eq!(a.error_mean(), 0.0, 1e-2);
-    assert_almost_eq!(a.skewness(), 2.0, 1e-2);
-    assert_almost_eq!(a.kurtosis(), 6.0, 4e-2);
-}
diff --git a/tests/mean.rs b/tests/mean.rs
index 79331d9..c8f4793 100644
--- a/tests/mean.rs
+++ b/tests/mean.rs
@@ -2,8 +2,6 @@
 
 extern crate core;
 
-extern crate rand;
-
 use core::iter::Iterator;
 
 use average::MeanWithError;
@@ -58,17 +56,3 @@ fn merge() {
         assert_eq!(avg_total.sample_variance(), avg_left.sample_variance());
     }
 }
-
-#[test]
-fn normal_distribution() {
-    use rand::distributions::{Normal, IndependentSample};
-    let normal = Normal::new(2.0, 3.0);
-    let mut a = MeanWithError::new();
-    for _ in 0..1_000_000 {
-        a.add(normal.ind_sample(&mut ::rand::thread_rng()));
-    }
-    assert_almost_eq!(a.mean(), 2.0, 1e-2);
-    assert_almost_eq!(a.sample_variance().sqrt(), 3.0, 1e-2);
-    assert_almost_eq!(a.population_variance().sqrt(), 3.0, 1e-2);
-    assert_almost_eq!(a.error(), 0.0, 1e-2);
-}
diff --git a/tests/random.rs b/tests/random.rs
new file mode 100644
index 0000000..4f8c62f
--- /dev/null
+++ b/tests/random.rs
@@ -0,0 +1,38 @@
+#[macro_use] extern crate average;
+
+extern crate rand;
+
+use average::Kurtosis;
+
+#[test]
+fn normal_distribution() {
+    use rand::distributions::{Normal, IndependentSample};
+    let normal = Normal::new(2.0, 3.0);
+    let mut a = Kurtosis::new();
+    for _ in 0..1_000_000 {
+        a.add(normal.ind_sample(&mut ::rand::thread_rng()));
+    }
+    assert_almost_eq!(a.mean(), 2.0, 1e-2);
+    assert_almost_eq!(a.sample_variance().sqrt(), 3.0, 1e-2);
+    assert_almost_eq!(a.population_variance().sqrt(), 3.0, 1e-2);
+    assert_almost_eq!(a.error_mean(), 0.0, 1e-2);
+    assert_almost_eq!(a.skewness(), 0.0, 1e-2);
+    assert_almost_eq!(a.kurtosis(), 0.0, 4e-2);
+}
+
+#[test]
+fn exponential_distribution() {
+    use rand::distributions::{Exp, IndependentSample};
+    let lambda = 2.0;
+    let normal = Exp::new(lambda);
+    let mut a = Kurtosis::new();
+    for _ in 0..6_000_000 {
+        a.add(normal.ind_sample(&mut ::rand::thread_rng()));
+    }
+    assert_almost_eq!(a.mean(), 1./lambda, 1e-2);
+    assert_almost_eq!(a.sample_variance().sqrt(), 1./lambda, 1e-2);
+    assert_almost_eq!(a.population_variance().sqrt(), 1./lambda, 1e-2);
+    assert_almost_eq!(a.error_mean(), 0.0, 1e-2);
+    assert_almost_eq!(a.skewness(), 2.0, 1e-2);
+    assert_almost_eq!(a.kurtosis(), 6.0, 1e-1);
+}
diff --git a/tests/skewness.rs b/tests/skewness.rs
index a8979d2..378aaae 100644
--- a/tests/skewness.rs
+++ b/tests/skewness.rs
@@ -2,8 +2,6 @@
 
 extern crate core;
 
-extern crate rand;
-
 use core::iter::Iterator;
 
 use average::Skewness;
@@ -55,19 +53,3 @@ fn merge() {
         assert_almost_eq!(avg_total.skewness(), avg_left.skewness(), 1e-14);
     }
 }
-
-#[test]
-fn exponential_distribution() {
-    use rand::distributions::{Exp, IndependentSample};
-    let lambda = 2.0;
-    let normal = Exp::new(lambda);
-    let mut a = Skewness::new();
-    for _ in 0..2_000_000 {
-        a.add(normal.ind_sample(&mut ::rand::thread_rng()));
-    }
-    assert_almost_eq!(a.mean(), 1./lambda, 1e-2);
-    assert_almost_eq!(a.sample_variance().sqrt(), 1./lambda, 1e-2);
-    assert_almost_eq!(a.population_variance().sqrt(), 1./lambda, 1e-2);
-    assert_almost_eq!(a.error_mean(), 0.0, 1e-2);
-    assert_almost_eq!(a.skewness(), 2.0, 1e-2);
-}