diff --git a/src/stcal/outlier_detection/utils.py b/src/stcal/outlier_detection/utils.py
index 88d27ce0c..bc0485f36 100644
--- a/src/stcal/outlier_detection/utils.py
+++ b/src/stcal/outlier_detection/utils.py
@@ -77,12 +77,7 @@ def compute_weight_threshold(weight, maskpt):
     weight_masked = np.ma.array(weight, mask=np.logical_or(
         mask_zero_weight, mask_nans))
     # Sigma-clip the unmasked data
-    weight_masked = sigma_clip(weight_masked,
-                               sigma=3,
-                               maxiters=5,
-                               masked=False,
-                               copy=False,
-                               )
+    weight_masked = sigma_clip(weight_masked, sigma=3, maxiters=5)
     mean_weight = np.mean(weight_masked)
     # Mask pixels where weight falls below maskpt percent
     weight_threshold = mean_weight * maskpt
diff --git a/tests/outlier_detection/test_utils.py b/tests/outlier_detection/test_utils.py
index 4112391bc..0a59f3c2e 100644
--- a/tests/outlier_detection/test_utils.py
+++ b/tests/outlier_detection/test_utils.py
@@ -16,7 +16,6 @@
     reproject,
     medfilt,
 )
-from stcal.testing_helpers import MemoryThreshold
 
 
 @pytest.mark.parametrize("shape,diff", [
@@ -73,23 +72,6 @@ def test_compute_weight_threshold_zeros():
     np.testing.assert_allclose(result, 21)
 
 
-def test_compute_weight_threshold_memory():
-    """Test that weight threshold function modifies
-    the weight array in place"""
-    arr = np.zeros([500, 500], dtype=np.float32)
-    arr[:250, :250] = 42
-    arr[10,10] = 0
-    arr[-10,-10] = np.nan
-
-    # buffer to account for memory overhead needs to be small enough
-    # to ensure that the array was not copied
-    fractional_memory_buffer = 1.9
-    expected_mem = int(arr.nbytes*fractional_memory_buffer)
-    with MemoryThreshold(str(expected_mem) + " B"):
-        result = compute_weight_threshold(arr, 0.5)
-    np.testing.assert_allclose(result, 21)
-
-
 def test_flag_crs():
     sci = np.zeros((10, 10), dtype=np.float32)
     err = np.ones_like(sci)