diff --git a/test/test_ops.py b/test/test_ops.py
index 8862e54a91..e1272a4efe 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -258,14 +258,21 @@ class TestOps(unittest.TestCase):
   def test_mean_axis(self):
     helper_test_op([(3,4,5,6)], lambda x: x.mean(axis=(1,2)), lambda x: Tensor.mean(x, axis=(1,2)))
   def test_std(self):
-    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, unbiased=False), lambda x: Tensor.std(x))
+    helper_test_op([(45, 65, 85)], lambda x: torch.std(x), lambda x: Tensor.std(x))
+    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, correction=0), lambda x: Tensor.std(x, correction=0))
+    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, correction=5), lambda x: Tensor.std(x, correction=5))
   def test_std_axis(self):
-    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, unbiased=False, dim=0), lambda x: Tensor.std(x, axis=0))
-    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, unbiased=False, dim=2), lambda x: Tensor.std(x, axis=2))
-    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, unbiased=False, dim=[1, 2]), lambda x: Tensor.std(x, axis=[1, 2]))
-    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, unbiased=False, dim=None), lambda x: Tensor.std(x, axis=None))
+    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, dim=0), lambda x: Tensor.std(x, axis=0))
+    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, dim=2), lambda x: Tensor.std(x, axis=2))
+    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, dim=[1, 2]), lambda x: Tensor.std(x, axis=[1, 2]))
+    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, dim=None), lambda x: Tensor.std(x, axis=None))
+    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, correction=0, dim=0), lambda x: Tensor.std(x, axis=0, correction=0))
+    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, correction=0, dim=2), lambda x: Tensor.std(x, axis=2, correction=0))
+    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, correction=0, dim=[1, 2]), lambda x: Tensor.std(x, axis=[1, 2], correction=0))
+    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, correction=0, dim=None), lambda x: Tensor.std(x, axis=None, correction=0))
   def test_std_keepdim(self):
     helper_test_op([(45, 65, 85)], lambda x: torch.std(x, keepdim=True), lambda x: Tensor.std(x, keepdim=True))
+    helper_test_op([(45, 65, 85)], lambda x: torch.std(x, keepdim=True, correction=0, dim=0), lambda x: Tensor.std(x, keepdim=True, correction=0, axis=0))
   def test_log_softmax(self):
     helper_test_op([(45,65)], lambda x: torch.nn.LogSoftmax(dim=1)(x), Tensor.log_softmax, atol=1e-7, grad_atol=1e-7)
   def test_log_softmax_other_axis(self):
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index b31abb9188..c4d8e2a9a7 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -336,10 +336,9 @@ class Tensor:
   def mean(self, axis=None, keepdim=False):
     out = self.sum(axis=axis, keepdim=keepdim)
     return out * (prod(out.shape)/prod(self.shape))
-  # TODO: implement unbiased True option for torch bessel's correction (subtracting 1 from divisor causes 0.01 error)
-  def std(self, axis=None, keepdim=False):
-    square_sum = ((self - self.mean(axis=axis, keepdim=True)).square()).sum(axis=axis, keepdim=keepdim)
-    return (square_sum * (prod(square_sum.shape)/prod(self.shape))).sqrt()
+  def std(self, axis=None, keepdim=False, correction=1):
+    square_sum = ((self - self.mean(axis=axis, keepdim=True)).square()).sum(axis=axis, keepdim=keepdim)                           
+    return (square_sum / (prod(self.shape)/prod(square_sum.shape)-correction)).sqrt()
   def _softmax(self, axis):
     m = self - self.max(axis=axis, keepdim=True)
     e = m.exp()