diff --git a/tests/ndarray_adapters_test.py b/tests/ndarray_adapters_test.py
index 2f38522..537ff0b 100644
--- a/tests/ndarray_adapters_test.py
+++ b/tests/ndarray_adapters_test.py
@@ -144,15 +144,40 @@ def test_adapter_positional_numpy_consistency(self, array_type, dtype):
     for fast in (True, False):
       with self.subTest("summary_fast" if fast else "summary_slow"):
         summary_info_np = helpers.ensure_text(
-            np_adapter.get_array_summary(array_np, fast=True)
+            np_adapter.get_array_summary(array_np, fast=fast)
         )
         summary_info = helpers.ensure_text(
-            cur_adapter.get_array_summary(array, fast=True)
+            cur_adapter.get_array_summary(array, fast=fast)
         )
         self.assertEqual(
             summary_info_np.split(" ", 1)[1], summary_info.split(" ", 1)[1]
         )
 
+  @parameterized.product(
+      array_type=["numpy", "jax", "torch"],
+      values=[
+          [1.0, 5.0],
+          [3.0],
+          [1.0, 5.0, np.nan],
+          [1.0, 5.0, np.nan, np.inf, -np.inf],
+      ],
+  )
+  def test_array_summary_standard_deviation(self, array_type, values):
+    array_np = np.array(values, dtype=np.float32)
+    if array_type == "numpy":
+      array = array_np
+    elif array_type == "jax":
+      array = jnp.array(array_np)
+    else:
+      array = torch.tensor(array_np)
+
+    adapter = type_registries.lookup_ndarray_adapter(array)
+    summary = helpers.ensure_text(adapter.get_array_summary(array, fast=False))
+    finite_values = array_np[np.isfinite(array_np)]
+    expected_mean = float(np.mean(finite_values))
+    expected_std = float(np.std(finite_values))
+    self.assertIn(f" ≈{expected_mean:.2} ±{expected_std:.2}", summary)
+
   def test_pytorch_named_axes_info(self):
     if not hasattr(torch.Tensor, "rename"):
       self.skipTest("PyTorch named tensors are not supported in this version")
diff --git a/tests/renderer_test.py b/tests/renderer_test.py
index e3f7ba9..892d8e9 100644
--- a/tests/renderer_test.py
+++ b/tests/renderer_test.py
@@ -413,11 +413,11 @@ def hook_that_crashes(node, path, node_renderer):
               np.arange(3 * 7, dtype=np.float32).reshape((3, 7))
           ).requires_grad_(),
           expected_collapsed=(
-              """<torch.Tensor float32(3, 7) ≈1e+01 ±3.7e+01 [≥0.0, ≤2e+01] zero:1 nonzero:20 requires_grad=True>"""
+              """<torch.Tensor float32(3, 7) ≈1e+01 ±6.1 [≥0.0, ≤2e+01] zero:1 nonzero:20 requires_grad=True>"""
           ),
           expected_expanded=textwrap.dedent(
               """\
-              # torch.Tensor float32(3, 7) ≈1e+01 ±3.7e+01 [≥0.0, ≤2e+01] zero:1 nonzero:20 requires_grad=True
+              # torch.Tensor float32(3, 7) ≈1e+01 ±6.1 [≥0.0, ≤2e+01] zero:1 nonzero:20 requires_grad=True
                 tensor([[ 0.,  1.,  2.,  3.,  4.,  5.,  6.],
                         [ 7.,  8.,  9., 10., 11., 12., 13.],
                         [14., 15., 16., 17., 18., 19., 20.]], requires_grad=True)"""
@@ -429,11 +429,11 @@ def hook_that_crashes(node, path, node_renderer):
               torch.tensor(np.arange(3 * 7, dtype=np.float32).reshape((3, 7)))
           ),
           expected_collapsed=(
-              """<torch.Tensor float32(3, 7) ≈1e+01 ±3.7e+01 [≥0.0, ≤2e+01] zero:1 nonzero:20 requires_grad=True grad=<Tensor>>"""
+              """<torch.Tensor float32(3, 7) ≈1e+01 ±6.1 [≥0.0, ≤2e+01] zero:1 nonzero:20 requires_grad=True grad=<Tensor>>"""
           ),
           expected_expanded=textwrap.dedent(
               """\
-              # torch.Tensor float32(3, 7) ≈1e+01 ±3.7e+01 [≥0.0, ≤2e+01] zero:1 nonzero:20 requires_grad=True grad=<Tensor>
+              # torch.Tensor float32(3, 7) ≈1e+01 ±6.1 [≥0.0, ≤2e+01] zero:1 nonzero:20 requires_grad=True grad=<Tensor>
                 tensor([[ 0.,  1.,  2.,  3.,  4.,  5.,  6.],
                         [ 7.,  8.,  9., 10., 11., 12., 13.],
                         [14., 15., 16., 17., 18., 19., 20.]], requires_grad=True)"""
@@ -445,11 +445,11 @@ def hook_that_crashes(node, path, node_renderer):
               torch.tensor(np.arange(3 * 7, dtype=np.float32).reshape((3, 7)))
           ),
           expected_collapsed=(
-              """<torch.nn.Parameter float32(3, 7) ≈1e+01 ±3.7e+01 [≥0.0, ≤2e+01] zero:1 nonzero:20>"""
+              """<torch.nn.Parameter float32(3, 7) ≈1e+01 ±6.1 [≥0.0, ≤2e+01] zero:1 nonzero:20>"""
           ),
           expected_expanded=textwrap.dedent(
               """\
-              # torch.nn.Parameter float32(3, 7) ≈1e+01 ±3.7e+01 [≥0.0, ≤2e+01] zero:1 nonzero:20
+              # torch.nn.Parameter float32(3, 7) ≈1e+01 ±6.1 [≥0.0, ≤2e+01] zero:1 nonzero:20
                 Parameter containing:
                 tensor([[ 0.,  1.,  2.,  3.,  4.,  5.,  6.],
                         [ 7.,  8.,  9., 10., 11., 12., 13.],
@@ -462,10 +462,10 @@ def hook_that_crashes(node, path, node_renderer):
               torch.tensor(np.arange(3 * 7, dtype=np.float32).reshape((3, 7)))
           ).requires_grad_(False),
           expected_collapsed=(
-              """<torch.nn.Parameter float32(3, 7) ≈1e+01 ±3.7e+01 [≥0.0, ≤2e+01] zero:1 nonzero:20 requires_grad=False>"""
+              """<torch.nn.Parameter float32(3, 7) ≈1e+01 ±6.1 [≥0.0, ≤2e+01] zero:1 nonzero:20 requires_grad=False>"""
           ),
           expected_expanded=textwrap.dedent("""\
-              # torch.nn.Parameter float32(3, 7) ≈1e+01 ±3.7e+01 [≥0.0, ≤2e+01] zero:1 nonzero:20 requires_grad=False
+              # torch.nn.Parameter float32(3, 7) ≈1e+01 ±6.1 [≥0.0, ≤2e+01] zero:1 nonzero:20 requires_grad=False
                 Parameter containing:
                 tensor([[ 0.,  1.,  2.,  3.,  4.,  5.,  6.],
                         [ 7.,  8.,  9., 10., 11., 12., 13.],
@@ -602,9 +602,9 @@ def hook_that_crashes(node, path, node_renderer):
               " foo=<torch.nn.Parameter float32(5,) ≈1.0 ±0.0 [≥1.0, ≤1.0]"
               " nonzero:5>, bar=torch.tensor([0., 0., 0., 0., 0.]),"
               " linear=Linear(in_features=10, out_features=10, training=True,"
-              " weight=<torch.nn.Parameter float32(10, 10) ≈-0.008 ±0.028"
+              " weight=<torch.nn.Parameter float32(10, 10) ≈-0.008 ±0.17"
               " [≥-0.32, ≤0.3] nonzero:100>, bias=<torch.nn.Parameter"
-              " float32(10,) ≈0.008 ±0.04 [≥-0.31, ≤0.31] nonzero:10>, ),"
+              " float32(10,) ≈0.008 ±0.2 [≥-0.31, ≤0.31] nonzero:10>, ),"
               " mod_list=ModuleList(training=True, (0):"
               " LayerNorm(normalized_shape=(10,), eps=1e-05,"
               " elementwise_affine=True, training=True,"
@@ -621,7 +621,7 @@ def hook_that_crashes(node, path, node_renderer):
                 # Buffers:
                 bar=torch.tensor([0., 0., 0., 0., 0.]),
                 # Child modules:
-                linear=Linear(in_features=10, out_features=10, training=True, weight=<torch.nn.Parameter float32(10, 10) ≈-0.008 ±0.028 [≥-0.32, ≤0.3] nonzero:100>, bias=<torch.nn.Parameter float32(10,) ≈0.008 ±0.04 [≥-0.31, ≤0.31] nonzero:10>, ),
+                linear=Linear(in_features=10, out_features=10, training=True, weight=<torch.nn.Parameter float32(10, 10) ≈-0.008 ±0.17 [≥-0.32, ≤0.3] nonzero:100>, bias=<torch.nn.Parameter float32(10,) ≈0.008 ±0.2 [≥-0.31, ≤0.31] nonzero:10>, ),
                 mod_list=ModuleList(training=True, (0): LayerNorm(normalized_shape=(10,), eps=1e-05, elementwise_affine=True, training=True, weight=<torch.nn.Parameter float32(10,) ≈1.0 ±0.0 [≥1.0, ≤1.0] nonzero:10>, bias=<torch.nn.Parameter float32(10,) ≈0.0 ±0.0 [≥0.0, ≤0.0] zero:10>, ), (1): SiLU(inplace=False, training=True, ), ),
               )"""),
           expected_roundtrip=textwrap.dedent("""\
@@ -632,7 +632,7 @@ def hook_that_crashes(node, path, node_renderer):
                 # Buffers:
                 bar=torch.tensor([0., 0., 0., 0., 0.]),
                 # Child modules:
-                linear=<torch.nn.modules.linear.Linear(in_features=10, out_features=10, training=True, weight=<torch.nn.Parameter float32(10, 10) ≈-0.008 ±0.028 [≥-0.32, ≤0.3] nonzero:100>, bias=<torch.nn.Parameter float32(10,) ≈0.008 ±0.04 [≥-0.31, ≤0.31] nonzero:10>, )>,
+                linear=<torch.nn.modules.linear.Linear(in_features=10, out_features=10, training=True, weight=<torch.nn.Parameter float32(10, 10) ≈-0.008 ±0.17 [≥-0.32, ≤0.3] nonzero:100>, bias=<torch.nn.Parameter float32(10,) ≈0.008 ±0.2 [≥-0.31, ≤0.31] nonzero:10>, )>,
                 mod_list=<torch.nn.modules.container.ModuleList(training=True, (0): <torch.nn.modules.normalization.LayerNorm(normalized_shape=(10,), eps=1e-05, elementwise_affine=True, training=True, weight=<torch.nn.Parameter float32(10,) ≈1.0 ±0.0 [≥1.0, ≤1.0] nonzero:10>, bias=<torch.nn.Parameter float32(10,) ≈0.0 ±0.0 [≥0.0, ≤0.0] zero:10>, )>, (1): <torch.nn.modules.activation.SiLU(inplace=False, training=True, )>, )>,
               )>"""),
       ),
@@ -657,8 +657,8 @@ def hook_that_crashes(node, path, node_renderer):
                 linear=Linear(
                   in_features=10, out_features=10, training=True,
                   # Parameters:
-                  weight=<torch.nn.Parameter float32(10, 10) ≈-0.008 ±0.028 [≥-0.32, ≤0.3] nonzero:100>,
-                  bias=<torch.nn.Parameter float32(10,) ≈0.008 ±0.04 [≥-0.31, ≤0.31] nonzero:10>,
+                  weight=<torch.nn.Parameter float32(10, 10) ≈-0.008 ±0.17 [≥-0.32, ≤0.3] nonzero:100>,
+                  bias=<torch.nn.Parameter float32(10,) ≈0.008 ±0.2 [≥-0.31, ≤0.31] nonzero:10>,
                 ),
                 mod_list=ModuleList(
                   training=True,
@@ -939,7 +939,7 @@ def hook_that_crashes(node, path, node_renderer):
           expected_collapsed="[<Param float32(3, 7)>]",
           expected_expanded=textwrap.dedent("""\
               [
-                <torch.nn.Parameter float32(3, 7) ≈1e+01 ±3.7e+01 [≥0.0, ≤2e+01] zero:1 nonzero:20>,
+                <torch.nn.Parameter float32(3, 7) ≈1e+01 ±6.1 [≥0.0, ≤2e+01] zero:1 nonzero:20>,
               ]"""),
       ),
   )
diff --git a/treescope/external/torch_support.py b/treescope/external/torch_support.py
index faf05b9..84d0a75 100644
--- a/treescope/external/torch_support.py
+++ b/treescope/external/torch_support.py
@@ -230,7 +230,7 @@ def get_array_summary(
         any_finite = torch.any(isfinite)
         inf_to_nan = torch.where(isfinite, array, torch.nan)
         mean = torch.nanmean(inf_to_nan)
-        std = torch.nanmean(torch.square(inf_to_nan - mean))
+        std = torch.sqrt(torch.nanmean(torch.square(inf_to_nan - mean)))
 
         if any_finite:
           summary_parts.append(f" ≈{float(mean):.2} ±{float(std):.2}")