diff --git a/test/test_transforms.py b/test/test_transforms.py
index 6dd4dfc3830..33d81c657da 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -1621,76 +1621,6 @@ def test_gaussian_blur_asserts(self):
         with self.assertRaisesRegex(ValueError, r"sigma should be a single number or a list/tuple with length 2"):
             transforms.GaussianBlur(3, "sigma_string")
 
-    def _test_randomness(self, fn, trans, configs):
-        random_state = random.getstate()
-        random.seed(42)
-        img = transforms.ToPILImage()(torch.rand(3, 16, 18))
-
-        for p in [0.5, 0.7]:
-            for config in configs:
-                inv_img = fn(img, **config)
-
-                num_samples = 250
-                counts = 0
-                for _ in range(num_samples):
-                    tranformation = trans(p=p, **config)
-                    tranformation.__repr__()
-                    out = tranformation(img)
-                    if out == inv_img:
-                        counts += 1
-
-                p_value = stats.binom_test(counts, num_samples, p=p)
-                random.setstate(random_state)
-                self.assertGreater(p_value, 0.0001)
-
-    @unittest.skipIf(stats is None, 'scipy.stats not available')
-    def test_random_invert(self):
-        self._test_randomness(
-            F.invert,
-            transforms.RandomInvert,
-            [{}]
-        )
-
-    @unittest.skipIf(stats is None, 'scipy.stats not available')
-    def test_random_posterize(self):
-        self._test_randomness(
-            F.posterize,
-            transforms.RandomPosterize,
-            [{"bits": 4}]
-        )
-
-    @unittest.skipIf(stats is None, 'scipy.stats not available')
-    def test_random_solarize(self):
-        self._test_randomness(
-            F.solarize,
-            transforms.RandomSolarize,
-            [{"threshold": 192}]
-        )
-
-    @unittest.skipIf(stats is None, 'scipy.stats not available')
-    def test_random_adjust_sharpness(self):
-        self._test_randomness(
-            F.adjust_sharpness,
-            transforms.RandomAdjustSharpness,
-            [{"sharpness_factor": 2.0}]
-        )
-
-    @unittest.skipIf(stats is None, 'scipy.stats not available')
-    def test_random_autocontrast(self):
-        self._test_randomness(
-            F.autocontrast,
-            transforms.RandomAutocontrast,
-            [{}]
-        )
-
-    @unittest.skipIf(stats is None, 'scipy.stats not available')
-    def test_random_equalize(self):
-        self._test_randomness(
-            F.equalize,
-            transforms.RandomEqualize,
-            [{}]
-        )
-
     def test_autoaugment(self):
         for policy in transforms.AutoAugmentPolicy:
             for fill in [None, 85, (128, 128, 128)]:
@@ -1834,6 +1764,36 @@ def test_pad_with_mode_F_images(self):
         assert_equal(padded_img.size, [edge_size + 2 * pad for edge_size in img.size], check_stride=False)
 
 
+@pytest.mark.skipif(stats is None, reason="scipy.stats not available")
+@pytest.mark.parametrize('fn, trans, config', [
+                        (F.invert, transforms.RandomInvert, {}),
+                        (F.posterize, transforms.RandomPosterize, {"bits": 4}),
+                        (F.solarize, transforms.RandomSolarize, {"threshold": 192}),
+                        (F.adjust_sharpness, transforms.RandomAdjustSharpness, {"sharpness_factor": 2.0}),
+                        (F.autocontrast, transforms.RandomAutocontrast, {}),
+                        (F.equalize, transforms.RandomEqualize, {})])
+@pytest.mark.parametrize('p', (.5, .7))
+def test_randomness(fn, trans, config, p):
+    random_state = random.getstate()
+    random.seed(42)
+    img = transforms.ToPILImage()(torch.rand(3, 16, 18))
+
+    inv_img = fn(img, **config)
+
+    num_samples = 250
+    counts = 0
+    for _ in range(num_samples):
+        tranformation = trans(p=p, **config)
+        tranformation.__repr__()
+        out = tranformation(img)
+        if out == inv_img:
+            counts += 1
+
+    p_value = stats.binom_test(counts, num_samples, p=p)
+    random.setstate(random_state)
+    assert p_value > 0.0001
+
+
 def test_adjust_brightness():
     x_shape = [2, 2, 3]
     x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]