diff --git a/rhodium/sampling.py b/rhodium/sampling.py
index dc39403..b159f6a 100644
--- a/rhodium/sampling.py
+++ b/rhodium/sampling.py
@@ -34,6 +34,8 @@ def sample_uniform(model, nsamples):
     
         result.append(entry)
         
+    return result
+        
 def sample_lhs(model, nsamples):
     """Returns a data set with uncertainty parameters sampled using Latin hypercube sampling."""
     if len(model.uncertainties) == 0:
diff --git a/rhodium/test/sampling_test.py b/rhodium/test/sampling_test.py
new file mode 100644
index 0000000..a957f1d
--- /dev/null
+++ b/rhodium/test/sampling_test.py
@@ -0,0 +1,48 @@
+# Copyright 2015-2016 David Hadka
+#
+# This file is part of Rhodium, a Python module for robust decision making and
+# exploratory modeling.
+#
+# Rhodium is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# Rhodium is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with Rhodium.  If not, see <http://www.gnu.org/licenses/>.
+from __future__ import division, print_function, absolute_import
+
+import six
+import unittest
+from rhodium import *
+
+class TestSampling(unittest.TestCase):
+    
+    def testUniform(self):   
+        model = Model("foo")
+        model.uncertainties = [UniformUncertainty("x", 5.0, 10.0)]
+        
+        samples = sample_uniform(model, 100)
+        
+        self.assertEquals(100, len(samples))
+
+        for i in range(len(samples)):
+            self.assertTrue("x" in samples[i])
+            self.assertTrue(samples[i]["x"] >= 5.0 and samples[i]["x"] <= 10.0)
+        
+    def testLHS(self):
+        model = Model("foo")
+        model.uncertainties = [UniformUncertainty("x", 5.0, 10.0)]
+        
+        samples = sample_lhs(model, 100)
+        
+        self.assertEquals(100, len(samples))
+
+        for i in range(len(samples)):
+            self.assertTrue("x" in samples[i])
+            self.assertTrue(samples[i]["x"] >= 5.0 and samples[i]["x"] <= 10.0)