diff --git a/tests/models/test_StandardScaler.py b/tests/models/test_StandardScaler.py
index ea371d6..9999f42 100644
--- a/tests/models/test_StandardScaler.py
+++ b/tests/models/test_StandardScaler.py
@@ -4,7 +4,7 @@
 import sklearn.preprocessing as sk_pp
 
 from diffprivlib.models.standard_scaler import StandardScaler
-from diffprivlib.utils import PrivacyLeakWarning, DiffprivlibCompatibilityWarning, BudgetError
+from diffprivlib.utils import PrivacyLeakWarning, DiffprivlibCompatibilityWarning, BudgetError, check_random_state
 
 
 class TestStandardScaler(TestCase):
@@ -65,12 +65,13 @@ def test_inf_epsilon(self):
         self.assertTrue(np.all(dp_ss.n_samples_seen_ == sk_ss.n_samples_seen_))
 
     def test_different_results(self):
-        X = np.random.rand(10, 5)
+        rng = check_random_state(1)
+        X = rng.random((10, 5))
 
-        ss1 = StandardScaler(bounds=(0, 1))
+        ss1 = StandardScaler(bounds=(0, 1), random_state=rng)
         ss1.fit(X)
 
-        ss2 = StandardScaler(bounds=(0, 1))
+        ss2 = StandardScaler(bounds=(0, 1), random_state=rng)
         ss2.fit(X)
 
         self.assertFalse(np.allclose(ss1.mean_, ss2.mean_), "Arrays %s and %s should be different" %
@@ -88,8 +89,8 @@ def test_functionality(self):
         self.assertIsNotNone(ss.fit_transform(X))
 
     def test_similar_results(self):
-        rng = np.random.RandomState(0)
-        X = rng.rand(100000, 5)
+        rng = check_random_state(0)
+        X = rng.random((100000, 5))
 
         dp_ss = StandardScaler(bounds=(0, 1), epsilon=float("inf"), random_state=rng)
         dp_ss.fit(X)
@@ -104,8 +105,8 @@ def test_similar_results(self):
         self.assertTrue(np.all(dp_ss.n_samples_seen_ == sk_ss.n_samples_seen_))
 
     def test_random_state(self):
-        rng = np.random.RandomState(0)
-        X = rng.rand(100000, 5)
+        rng = check_random_state(0)
+        X = rng.random((100000, 5))
 
         ss0 = StandardScaler(bounds=(0, 1), epsilon=1, random_state=0)
         ss1 = StandardScaler(bounds=(0, 1), epsilon=1, random_state=1)