diff --git a/pandas/tests/window/common.py b/pandas/tests/window/common.py
index c3648bc619c50..77f59bf919168 100644
--- a/pandas/tests/window/common.py
+++ b/pandas/tests/window/common.py
@@ -212,40 +212,23 @@ def _create_data(self):
         super()._create_data()
         self.data = _consistency_data
 
-    def _test_moments_consistency(
-        self,
-        min_periods,
-        count,
-        mean,
-        mock_mean,
-        corr,
-        var_unbiased=None,
-        std_unbiased=None,
-        cov_unbiased=None,
-        var_biased=None,
-        std_biased=None,
-        cov_biased=None,
-        var_debiasing_factors=None,
-    ):
-        def _non_null_values(x):
-            values = x.values.ravel()
-            return set(values[notna(values)].tolist())
-
+    def _test_moments_consistency_mock_mean(self, mean, mock_mean):
         for (x, is_constant, no_nans) in self.data:
-            count_x = count(x)
             mean_x = mean(x)
+            # check that correlation of a series with itself is either 1 or NaN
 
             if mock_mean:
                 # check that mean equals mock_mean
                 expected = mock_mean(x)
                 tm.assert_equal(mean_x, expected.astype("float64"))
 
+    def _test_moments_consistency_is_constant(self, min_periods, count, mean, corr):
+        for (x, is_constant, no_nans) in self.data:
+            count_x = count(x)
+            mean_x = mean(x)
             # check that correlation of a series with itself is either 1 or NaN
             corr_x_x = corr(x, x)
 
-            # assert _non_null_values(corr_x_x).issubset(set([1.]))
-            # restore once rolling_cov(x, x) is identically equal to var(x)
-
             if is_constant:
                 exp = x.max() if isinstance(x, Series) else x.max().max()
 
@@ -258,6 +241,10 @@ def _non_null_values(x):
                 expected[:] = np.nan
                 tm.assert_equal(corr_x_x, expected)
 
+    def _test_moments_consistency_var_debiasing_factors(
+        self, var_biased=None, var_unbiased=None, var_debiasing_factors=None
+    ):
+        for (x, is_constant, no_nans) in self.data:
             if var_unbiased and var_biased and var_debiasing_factors:
                 # check variance debiasing factors
                 var_unbiased_x = var_unbiased(x)
@@ -265,6 +252,24 @@ def _non_null_values(x):
                 var_debiasing_factors_x = var_debiasing_factors(x)
                 tm.assert_equal(var_unbiased_x, var_biased_x * var_debiasing_factors_x)
 
+    def _test_moments_consistency(
+        self,
+        min_periods,
+        count,
+        mean,
+        corr,
+        var_unbiased=None,
+        std_unbiased=None,
+        cov_unbiased=None,
+        var_biased=None,
+        std_biased=None,
+        cov_biased=None,
+    ):
+
+        for (x, is_constant, no_nans) in self.data:
+            count_x = count(x)
+            mean_x = mean(x)
+
             for (std, var, cov) in [
                 (std_biased, var_biased, cov_biased),
                 (std_unbiased, var_unbiased, cov_unbiased),
diff --git a/pandas/tests/window/moments/test_moments_ewm.py b/pandas/tests/window/moments/test_moments_ewm.py
index bf2bd1420b7f4..489c1ff14ecfd 100644
--- a/pandas/tests/window/moments/test_moments_ewm.py
+++ b/pandas/tests/window/moments/test_moments_ewm.py
@@ -379,6 +379,43 @@ def _ewma(s, com, min_periods, adjust, ignore_na):
             return result
 
         com = 3.0
+        self._test_moments_consistency_mock_mean(
+            mean=lambda x: x.ewm(
+                com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
+            ).mean(),
+            mock_mean=lambda x: _ewma(
+                x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
+            ),
+        )
+
+        self._test_moments_consistency_is_constant(
+            min_periods=min_periods,
+            count=lambda x: x.expanding().count(),
+            mean=lambda x: x.ewm(
+                com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
+            ).mean(),
+            corr=lambda x, y: x.ewm(
+                com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
+            ).corr(y),
+        )
+
+        self._test_moments_consistency_var_debiasing_factors(
+            var_unbiased=lambda x: (
+                x.ewm(
+                    com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
+                ).var(bias=False)
+            ),
+            var_biased=lambda x: (
+                x.ewm(
+                    com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
+                ).var(bias=True)
+            ),
+            var_debiasing_factors=lambda x: (
+                _variance_debiasing_factors(
+                    x, com=com, adjust=adjust, ignore_na=ignore_na
+                )
+            ),
+        )
         # test consistency between different ewm* moments
         self._test_moments_consistency(
             min_periods=min_periods,
@@ -386,9 +423,6 @@ def _ewma(s, com, min_periods, adjust, ignore_na):
             mean=lambda x: x.ewm(
                 com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
             ).mean(),
-            mock_mean=lambda x: _ewma(
-                x, com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
-            ),
             corr=lambda x, y: x.ewm(
                 com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
             ).corr(y),
@@ -420,9 +454,4 @@ def _ewma(s, com, min_periods, adjust, ignore_na):
                     com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
                 ).cov(y, bias=True)
             ),
-            var_debiasing_factors=lambda x: (
-                _variance_debiasing_factors(
-                    x, com=com, adjust=adjust, ignore_na=ignore_na
-                )
-            ),
         )
diff --git a/pandas/tests/window/moments/test_moments_expanding.py b/pandas/tests/window/moments/test_moments_expanding.py
index 3361ecab28669..d311937e234d8 100644
--- a/pandas/tests/window/moments/test_moments_expanding.py
+++ b/pandas/tests/window/moments/test_moments_expanding.py
@@ -328,12 +328,31 @@ def test_expanding_consistency(self, min_periods):
             )
 
             # test consistency between different expanding_* moments
+            self._test_moments_consistency_mock_mean(
+                mean=lambda x: x.expanding(min_periods=min_periods).mean(),
+                mock_mean=lambda x: x.expanding(min_periods=min_periods).sum()
+                / x.expanding().count(),
+            )
+
+            self._test_moments_consistency_is_constant(
+                min_periods=min_periods,
+                count=lambda x: x.expanding().count(),
+                mean=lambda x: x.expanding(min_periods=min_periods).mean(),
+                corr=lambda x, y: x.expanding(min_periods=min_periods).corr(y),
+            )
+
+            self._test_moments_consistency_var_debiasing_factors(
+                var_unbiased=lambda x: x.expanding(min_periods=min_periods).var(),
+                var_biased=lambda x: x.expanding(min_periods=min_periods).var(ddof=0),
+                var_debiasing_factors=lambda x: (
+                    x.expanding().count()
+                    / (x.expanding().count() - 1.0).replace(0.0, np.nan)
+                ),
+            )
             self._test_moments_consistency(
                 min_periods=min_periods,
                 count=lambda x: x.expanding().count(),
                 mean=lambda x: x.expanding(min_periods=min_periods).mean(),
-                mock_mean=lambda x: x.expanding(min_periods=min_periods).sum()
-                / x.expanding().count(),
                 corr=lambda x, y: x.expanding(min_periods=min_periods).corr(y),
                 var_unbiased=lambda x: x.expanding(min_periods=min_periods).var(),
                 std_unbiased=lambda x: x.expanding(min_periods=min_periods).std(),
@@ -343,10 +362,6 @@ def test_expanding_consistency(self, min_periods):
                 cov_biased=lambda x, y: x.expanding(min_periods=min_periods).cov(
                     y, ddof=0
                 ),
-                var_debiasing_factors=lambda x: (
-                    x.expanding().count()
-                    / (x.expanding().count() - 1.0).replace(0.0, np.nan)
-                ),
             )
 
             # test consistency between expanding_xyz() and either (a)
diff --git a/pandas/tests/window/moments/test_moments_rolling.py b/pandas/tests/window/moments/test_moments_rolling.py
index 631b13f874ca7..c110ed172ecb9 100644
--- a/pandas/tests/window/moments/test_moments_rolling.py
+++ b/pandas/tests/window/moments/test_moments_rolling.py
@@ -945,9 +945,7 @@ def test_rolling_consistency(self, window, min_periods, center):
             )
 
             # test consistency between different rolling_* moments
-            self._test_moments_consistency(
-                min_periods=min_periods,
-                count=lambda x: (x.rolling(window=window, center=center).count()),
+            self._test_moments_consistency_mock_mean(
                 mean=lambda x: (
                     x.rolling(
                         window=window, min_periods=min_periods, center=center
@@ -962,6 +960,53 @@ def test_rolling_consistency(self, window, min_periods, center):
                         ).count()
                     )
                 ),
+            )
+
+            self._test_moments_consistency_is_constant(
+                min_periods=min_periods,
+                count=lambda x: (x.rolling(window=window, center=center).count()),
+                mean=lambda x: (
+                    x.rolling(
+                        window=window, min_periods=min_periods, center=center
+                    ).mean()
+                ),
+                corr=lambda x, y: (
+                    x.rolling(
+                        window=window, min_periods=min_periods, center=center
+                    ).corr(y)
+                ),
+            )
+
+            self._test_moments_consistency_var_debiasing_factors(
+                var_unbiased=lambda x: (
+                    x.rolling(
+                        window=window, min_periods=min_periods, center=center
+                    ).var()
+                ),
+                var_biased=lambda x: (
+                    x.rolling(
+                        window=window, min_periods=min_periods, center=center
+                    ).var(ddof=0)
+                ),
+                var_debiasing_factors=lambda x: (
+                    x.rolling(window=window, center=center)
+                    .count()
+                    .divide(
+                        (x.rolling(window=window, center=center).count() - 1.0).replace(
+                            0.0, np.nan
+                        )
+                    )
+                ),
+            )
+
+            self._test_moments_consistency(
+                min_periods=min_periods,
+                count=lambda x: (x.rolling(window=window, center=center).count()),
+                mean=lambda x: (
+                    x.rolling(
+                        window=window, min_periods=min_periods, center=center
+                    ).mean()
+                ),
                 corr=lambda x, y: (
                     x.rolling(
                         window=window, min_periods=min_periods, center=center
@@ -997,15 +1042,6 @@ def test_rolling_consistency(self, window, min_periods, center):
                         window=window, min_periods=min_periods, center=center
                     ).cov(y, ddof=0)
                 ),
-                var_debiasing_factors=lambda x: (
-                    x.rolling(window=window, center=center)
-                    .count()
-                    .divide(
-                        (x.rolling(window=window, center=center).count() - 1.0).replace(
-                            0.0, np.nan
-                        )
-                    )
-                ),
             )
 
             # test consistency between rolling_xyz() and either (a)