diff --git a/act/qc/qcfilter.py b/act/qc/qcfilter.py
index ab4b252306..a3f99a4dc3 100644
--- a/act/qc/qcfilter.py
+++ b/act/qc/qcfilter.py
@@ -942,9 +942,9 @@ def datafilter(
                 ds.qcfilter.datafilter(rm_assessments="Bad")
                 ds_2 = ds.mean()
 
-                print("All_data=", ds_1[var_name].values)
+                print("All_data =", ds_1[var_name].values)
                 All_data = 98.86098
-                print("Bad_Removed=", ds_2[var_name].values)
+                print("Bad_Removed =", ds_2[var_name].values)
                 Bad_Removed = 99.15148
 
         """
@@ -958,10 +958,18 @@ def datafilter(
             qc_var_name = self.check_for_ancillary_qc(var_name, add_if_missing=False, cleanup=False)
             if qc_var_name is None:
                 if verbose:
-                    print(
-                        f'No quality control variable for {var_name} found '
-                        f'in call to .qcfilter.datafilter()'
-                    )
+                    if var_name in ['base_time', 'time_offset']:
+                        continue
+
+                    try:
+                        if self._obj[var_name].attrs['standard_name'] == 'quality_flag':
+                            continue
+                    except KeyError:
+                        pass
+
+                    print(f'No quality control variable for {var_name} found '
+                          f'in call to .qcfilter.datafilter()')
+
                 continue
 
             data = self.get_masked_data(
@@ -971,8 +979,16 @@ def datafilter(
                 ma_fill_value=np_ma,
             )
 
-            self._obj[var_name].values = data
+            # If data was orginally stored as Dask array return values to Dataset as Dask array
+            # else set as Numpy array.
+            try:
+                self._obj[var_name].data = dask.array.from_array(
+                    data, chunks=self._obj[var_name].data.chunksize)
+
+            except AttributeError:
+                self._obj[var_name].values = data
 
+            # If requested delete quality control variable
             if del_qc_var:
                 del self._obj[qc_var_name]
                 if verbose:
diff --git a/act/tests/test_qc.py b/act/tests/test_qc.py
index 8011704a7e..677ab02a98 100644
--- a/act/tests/test_qc.py
+++ b/act/tests/test_qc.py
@@ -718,21 +718,40 @@ def test_qctests_dos():
 
 
 def test_datafilter():
-    ds = read_netcdf(EXAMPLE_MET1)
+    ds = read_netcdf(EXAMPLE_MET1, drop_variables=['base_time', 'time_offset'])
     ds.clean.cleanup()
 
+    data_var_names = list(ds.data_vars)
+    qc_var_names = [var_name for var_name in ds.data_vars if var_name.startswith('qc_')]
+    data_var_names = list(set(data_var_names) - set(qc_var_names))
+    data_var_names.sort()
+    qc_var_names.sort()
+
     var_name = 'atmos_pressure'
 
     ds_1 = ds.mean()
 
     ds.qcfilter.add_less_test(var_name, 99, test_assessment='Bad')
-    ds.qcfilter.datafilter(rm_assessments='Bad')
-    ds_2 = ds.mean()
-
+    ds_filtered = copy.deepcopy(ds)
+    ds_filtered.qcfilter.datafilter(rm_assessments='Bad', del_qc_var=False)
+    ds_2 = ds_filtered.mean()
     assert np.isclose(ds_1[var_name].values, 98.86, atol=0.01)
     assert np.isclose(ds_2[var_name].values, 99.15, atol=0.01)
+    assert isinstance(ds_1[var_name].data, da.core.Array)
+
+    ds_filtered = copy.deepcopy(ds)
+    ds_filtered.qcfilter.datafilter(rm_assessments='Bad', variables=var_name)
+    ds_2 = ds_filtered.mean()
+    assert np.isclose(ds_2[var_name].values, 99.15, atol=0.01)
+    expected_var_names = sorted(list(set(data_var_names + qc_var_names) - set(['qc_' + var_name])))
+    assert sorted(list(ds_filtered.data_vars)) == expected_var_names
+
+    ds_filtered = copy.deepcopy(ds)
+    ds_filtered.qcfilter.datafilter(rm_assessments='Bad', del_qc_var=True)
+    assert sorted(list(ds_filtered.data_vars)) == data_var_names
 
     ds.close()
+    del ds
 
 
 def test_qc_remainder():