diff --git a/test/backend/test_randomness.py b/test/backend/test_randomness.py
index 5ac503b812..adb03f0873 100644
--- a/test/backend/test_randomness.py
+++ b/test/backend/test_randomness.py
@@ -368,8 +368,8 @@ class TestRandomness(unittest.TestCase):
     @TinyJit
     def sample_one(): return Tensor(w).multinomial(1, replacement=False).realize()
 
-    tiny_samples = [sample_one().item() for _ in range(1000)]
-    torch_samples = [torch.tensor(w).multinomial(1, replacement=False).item() for _ in range(1000)]
+    tiny_samples = [sample_one().item() for _ in range(400)]
+    torch_samples = [torch.tensor(w).multinomial(1, replacement=False).item() for _ in range(400)]
     self.assertTrue(equal_distribution(lambda *_: Tensor(tiny_samples), lambda _: torch.tensor(torch_samples)))
 
     w = list(range(32))
@@ -384,8 +384,8 @@ class TestRandomness(unittest.TestCase):
     @TinyJit
     def sample_three(): return Tensor(w).multinomial(3, replacement=False).realize()
 
-    tiny_draws = np.array([sample_three().numpy() for _ in range(1000)])
-    torch_draws = np.array([torch.tensor(w).multinomial(3, replacement=False).numpy() for _ in range(1000)])
+    tiny_draws = np.array([sample_three().numpy() for _ in range(400)])
+    torch_draws = np.array([torch.tensor(w).multinomial(3, replacement=False).numpy() for _ in range(400)])
     for pos in range(3):
       self.assertTrue(equal_distribution(lambda *_: Tensor(tiny_draws[:, pos]), lambda _: torch.tensor(torch_draws[:, pos])))
 
@@ -415,7 +415,7 @@ class TestRandomness(unittest.TestCase):
   def test_rand_chain(self):
     # NOTE: this fails if property propagates deeper than stack limit
     for _ in range(833): Tensor.rand(1)
-    Tensor.rand(1).realize()
+    Tensor.rand(1).schedule_linear()
 
   def test_random_counter_overflow(self):
     device = Device.DEFAULT
diff --git a/test/backend/test_schedule.py b/test/backend/test_schedule.py
index 102c772565..cf327cc06c 100644
--- a/test/backend/test_schedule.py
+++ b/test/backend/test_schedule.py
@@ -5,7 +5,7 @@
 import gc, unittest, functools
 import numpy as np
 from typing import cast
-from hypothesis import assume, given, settings, strategies as strat
+from hypothesis import assume, given, strategies as strat
 
 from tinygrad import nn, dtypes, Device, Tensor, Variable
 from tinygrad.device import is_dtype_supported
@@ -113,12 +113,6 @@ class TestSchedule(unittest.TestCase):
     run_linear(*check_schedule(b, 1))
     np.testing.assert_allclose(b.numpy(), np.broadcast_to(a.numpy().astype(np.float16), (2, 4, 4))+2, rtol=1e-3)
 
-  def test_indexing_scalars_simple(self):
-    X = Tensor.randn(2, 2).realize()
-    xt = X[Tensor(1)][Tensor(0)]
-    run_linear(*check_schedule(xt, 1))
-    np.testing.assert_equal(xt.numpy(), X.numpy()[1][0])
-
   @unittest.skipIf(CI and Device.DEFAULT == "NV", "crashes on NV CI")
   def test_add_chain_buffers(self):
     N = 31
@@ -130,14 +124,14 @@ class TestSchedule(unittest.TestCase):
         root = root + functools.reduce(lambda a,b:a+b, bufs[i:i+X])
       self.assertEqual(root.item(), sum(range(N)))
 
-  @given(strat.sampled_from(range(2,4)), strat.sampled_from(range(2,4)), strat.sampled_from(range(0,4)), strat.sampled_from(range(0,4)))
-  @settings(deadline=None)
-  def test_indexing_scalars(self, x, y, a, b):
-    assume(a<x and b<y)
-    X = Tensor.randn(x, y).realize()
-    xt = X[Tensor(a)][Tensor(b)]
-    run_linear(*check_schedule(xt, 1))
-    np.testing.assert_equal(xt.numpy(), X.numpy()[a][b])
+  def test_indexing_scalars(self):
+    # cover each shape at all index corners
+    for x, y in [(2,2), (2,3), (3,2), (3,3)]:
+      for a, b in [(0,0), (0,y-1), (x-1,0), (x-1,y-1)]:
+        X = Tensor.randn(x, y).realize()
+        xt = X[Tensor(a)][Tensor(b)]
+        run_linear(*check_schedule(xt, 1))
+        np.testing.assert_equal(xt.numpy(), X.numpy()[a][b])
 
   def test_push_pads_elementwise(self):
     x = Tensor.full((4,4), 2.).contiguous().realize()