diff --git a/pyerrors/obs.py b/pyerrors/obs.py
index 168f7292..c29c1bd8 100644
--- a/pyerrors/obs.py
+++ b/pyerrors/obs.py
@@ -1358,7 +1358,7 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
         if (1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], _merge_idx([obs1.idl[name], obs2.idl[name]])]]))):
             raise Exception('Shapes of ensemble', name, 'do not fit')
 
-    if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):
+    if not hasattr(obs1, 'e_dvalue') or not hasattr(obs2, 'e_dvalue'):
         raise Exception('The gamma method has to be applied to both Obs first.')
 
     dvalue = 0
@@ -1452,7 +1452,7 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
     if set(obs1.names).isdisjoint(set(obs2.names)):
         return 0.
 
-    if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):
+    if not hasattr(obs1, 'e_dvalue') or not hasattr(obs2, 'e_dvalue'):
         raise Exception('The gamma method has to be applied to both Obs first.')
 
     dvalue = 0
diff --git a/tests/covobs_test.py b/tests/covobs_test.py
index 6f0e47f1..beedb54c 100644
--- a/tests/covobs_test.py
+++ b/tests/covobs_test.py
@@ -37,6 +37,7 @@ def test_covobs():
         op.gamma_method()
         assert(np.isclose(oc.value, op.value, rtol=1e-14, atol=1e-14))
 
+    [o.gamma_method() for o in cl]
     assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
     assert(pe.covariance2(cl[0], cl[1]) == cov[1][0])
 
diff --git a/tests/fits_test.py b/tests/fits_test.py
index 54c15ef4..b37abc25 100644
--- a/tests/fits_test.py
+++ b/tests/fits_test.py
@@ -274,6 +274,41 @@ def test_r_value_persistence():
     assert np.isclose(fitp[1].value, fitp[1].r_values['b'])
 
 
+def test_prior_fit():
+    def f(a, x):
+        return a[0] + a[1] * x
+
+    a = pe.pseudo_Obs(0.0, 0.1, 'a')
+    b = pe.pseudo_Obs(1.0, 0.2, 'a')
+
+    y = [a, b]
+    with pytest.raises(Exception):
+        fitp = pe.fits.least_squares([0, 1], 1 * np.array(y), f, priors=['0.0(8)', '1.0(8)'])
+
+    [o.gamma_method() for o in y]
+
+    fitp = pe.fits.least_squares([0, 1], y, f, priors=['0.0(8)', '1.0(8)'])
+    fitp = pe.fits.least_squares([0, 1], y, f, priors=y, resplot=True, qqplot=True)
+
+
+def test_error_band():
+    def f(a, x):
+        return a[0] + a[1] * x
+
+    a = pe.pseudo_Obs(0.0, 0.1, 'a')
+    b = pe.pseudo_Obs(1.0, 0.2, 'a')
+
+    x = [0, 1]
+    y = [a, b]
+
+    fitp = pe.fits.least_squares(x, y, f)
+
+    with pytest.raises(Exception):
+        pe.fits.error_band(x, f, fitp.fit_parameters)
+    fitp.gamma_method()
+    pe.fits.error_band(x, f, fitp.fit_parameters)
+
+
 def fit_general(x, y, func, silent=False, **kwargs):
     """Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.