diff --git a/examples/01_basic_example.ipynb b/examples/01_basic_example.ipynb
index 703e13b5..af868589 100644
--- a/examples/01_basic_example.ipynb
+++ b/examples/01_basic_example.ipynb
@@ -233,7 +233,7 @@
    ],
    "source": [
     "c_obs3 = np.sin(c_obs1 / c_obs2 - 1)\n",
-    "c_obs3.gamma_method()\n",
+    "c_obs3.gm()  # gm is a short alias for gamma_method\n",
     "c_obs3.details()"
    ]
   },
diff --git a/pyerrors/__init__.py b/pyerrors/__init__.py
index 981e0fd7..a65c9a80 100644
--- a/pyerrors/__init__.py
+++ b/pyerrors/__init__.py
@@ -41,7 +41,7 @@ print(my_new_obs)                             # Print the result to stdout
 `pyerrors` introduces a new datatype, `Obs`, which simplifies error propagation and estimation for auto- and cross-correlated data.
 An `Obs` object can be initialized with two arguments, the first is a list containing the samples for an observable from a Monte Carlo chain.
 The samples can either be provided as python list or as numpy array.
-The second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble. **It is crucial for the correct error propagation that observations from the same Monte Carlo history are labeled with the same name. See [Multiple ensembles/replica](#Multiple-ensembles/replica) for details.**
+The second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble. **It is crucial for the correct error propagation that observations from the same Monte Carlo history are labeled with the same name. See [Multiple ensembles/replica](#Multiple-ensemblesreplica) for details.**
 
 ```python
 import pyerrors as pe