diff --git a/examples/01_basic_example.ipynb b/examples/01_basic_example.ipynb
index df1d76f2..27bd712d 100644
--- a/examples/01_basic_example.ipynb
+++ b/examples/01_basic_example.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Import pyerrors, as well as autograd wrapped numpy and matplotlib."
+    "Import numpy, matplotlib and pyerrors."
    ]
   },
   {
@@ -91,7 +91,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`pyerrors` overloads all basic math operations, the user can work with these `Obs` as if they were real numbers. The proper resampling is performed in the background via automatic differentiation."
+    "`pyerrors` overloads all basic math operations, the user can work with these `Obs` as if they were real numbers. The proper error propagation is performed in the background via automatic differentiation."
    ]
   },
   {
@@ -179,7 +179,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This figure shows the windowsize dependence of the integrated autocorrelation time. The red vertical line signalizes the window chosen by the automatic windowing procedure with $S=2.0$.\n",
+    "This figure shows the windowsize dependence of the integrated autocorrelation time. The vertical line signalizes the window chosen by the automatic windowing procedure with $S=2.0$.\n",
     "Choosing a larger windowsize would not significantly alter $\\tau_\\text{int}$, so everything seems to be correct here."
    ]
   },
@@ -283,7 +283,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now redo the error analysis and alter the value of S or attach a tail to the autocorrelation function to take into account long range autocorrelations"
+    "We can now redo the error analysis and alter the value of S or attach a tail to the autocorrelation function via the parameter `tau_exp` to take into account long range autocorrelations"
    ]
   },
   {
diff --git a/examples/02_correlators.ipynb b/examples/02_correlators.ipynb
index e2d2a09a..66c369cc 100644
--- a/examples/02_correlators.ipynb
+++ b/examples/02_correlators.ipynb
@@ -242,7 +242,7 @@
    "id": "4a9d13b2",
    "metadata": {},
    "source": [
-    "We can also use the priodicity of the lattice in order to obtain the cosh effective mass"
+    "We can also use the periodicity of the lattice in order to obtain the cosh effective mass"
    ]
   },
   {
@@ -387,8 +387,7 @@
    "source": [
     "## Missing Values \n",
     "\n",
-    "Apart from the build-in functions, there is another reason, why one should use a **Corr** instead of a list of **Obs**. \n",
-    "Missing values are handled for you. \n",
+    "Apart from the build-in functions, another benefit of using ``Corr`` objects is that they can handle missing values. \n",
     "We will create a second correlator with missing values. "
    ]
   },
@@ -459,18 +458,6 @@
     "The important thing is that, whatever you do, correlators keep their length **T**. So there will never be confusion about how you count timeslices. You can also take a plateau or perform a fit, even though some values might be missing."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "f3c4609c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "assert first_derivative.T == my_correlator.T == len(first_derivative.content) == len(my_correlator.content)\n",
-    "assert first_derivative.content[0] is None\n",
-    "assert first_derivative.content[-1] is None"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "7fcbcac4",