diff --git a/pyerrors/obs.py b/pyerrors/obs.py
index d211c541..42ae3b8c 100644
--- a/pyerrors/obs.py
+++ b/pyerrors/obs.py
@@ -426,6 +426,21 @@ class Obs:
                 my_string_list.append(my_string)
             print('\n'.join(my_string_list))
 
+    def reweight(self, rw):
+        """Reweight the obs with given rewighting factors.
+
+        Parameters
+        ----------
+        weight : Obs
+            Reweighting factor. An Observable that has to be defined on a superset of the
+            configurations in obs[i].idl for all i.
+        all_configs : bool
+            if True, the reweighted observables are normalized by the average of
+            the reweighting factor on all configurations in weight.idl and not
+            on the configurations in obs[i].idl. Default False.
+        """
+        return reweight(rw, [self])[0]
+
     def is_zero_within_error(self, sigma=1):
         """Checks whether the observable is zero within 'sigma' standard errors.
 
@@ -1330,7 +1345,7 @@ def reweight(weight, obs, **kwargs):
     all_configs : bool
         if True, the reweighted observables are normalized by the average of
         the reweighting factor on all configurations in weight.idl and not
-        on the configurations in obs[i].idl.
+        on the configurations in obs[i].idl. Default False.
     """
     result = []
     for i in range(len(obs)):
diff --git a/tests/obs_test.py b/tests/obs_test.py
index 5b6a8509..1665ef62 100644
--- a/tests/obs_test.py
+++ b/tests/obs_test.py
@@ -858,7 +858,7 @@ def test_covariance_additional_non_overlapping_data():
     assert np.isclose(corr1[0, 1], corr2[0, 1], atol=1e-14)
 
 
-def test_coavariance_reorder_non_overlapping_data():
+def test_covariance_reorder_non_overlapping_data():
     range1 = range(1, 20, 2)
     range2 = range(1, 41, 2)
 
@@ -884,6 +884,12 @@ def test_empty_obs():
     assert q == o
 
 
+def test_reweight_method():
+    obs1 = pe.pseudo_Obs(0.2, 0.01, 'test')
+    rw = pe.pseudo_Obs(0.999, 0.001, 'test')
+    assert obs1.reweight(rw) == pe.reweight(rw, [obs1])[0]
+
+
 def test_jackknife():
     full_data = np.random.normal(1.1, 0.87, 5487)