diff --git a/Orange/statistics/util.py b/Orange/statistics/util.py
index 57c38bbdc9e..7dc56490146 100644
--- a/Orange/statistics/util.py
+++ b/Orange/statistics/util.py
@@ -361,7 +361,9 @@ def weighted_mean():
             X.shape[0] - non_zero,
             non_zero))
     else:
-        nans = (~X.astype(bool)).sum(axis=0) if X.size else np.zeros(X.shape[1])
+        X_str = X.astype(str)
+        nans = ((X_str == "nan") | (X_str == "")).sum(axis=0) \
+            if X.size else np.zeros(X.shape[1])
         return np.column_stack((
             np.tile(np.inf, X.shape[1]),
             np.tile(-np.inf, X.shape[1]),
diff --git a/Orange/tests/test_statistics.py b/Orange/tests/test_statistics.py
index 59a04e9e29c..1c0b4226ccd 100644
--- a/Orange/tests/test_statistics.py
+++ b/Orange/tests/test_statistics.py
@@ -118,13 +118,14 @@ def test_stats_weights_sparse(self):
 
     def test_stats_non_numeric(self):
         X = np.array([
-            ['', 'a', 'b'],
-            ['a', '', 'b'],
-            ['a', 'b', ''],
+            ["", "a", np.nan, 0],
+            ["a", "", np.nan, 1],
+            ["a", "b", 0, 0],
         ], dtype=object)
         np.testing.assert_equal(stats(X), [[np.inf, -np.inf, 0, 0, 1, 2],
                                            [np.inf, -np.inf, 0, 0, 1, 2],
-                                           [np.inf, -np.inf, 0, 0, 1, 2]])
+                                           [np.inf, -np.inf, 0, 0, 2, 1],
+                                           [np.inf, -np.inf, 0, 0, 0, 3]])
 
     def test_nanmin_nanmax(self):
         warnings.filterwarnings("ignore", r".*All-NaN slice encountered.*")