diff --git a/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb b/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb
index a602028..9800dba 100644
--- a/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb
+++ b/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb
@@ -912,7 +912,7 @@
     "\n",
     "samples = samples.with_paths([(\"gaussian\", \"centre\")])\n",
     "\n",
-    "print(\"All parameters of the very first sample (containing only the Gaussian centre.\")\n",
+    "print(\"All parameters of the very first sample (containing only the Gaussian centre).\")\n",
     "print(samples.parameter_lists[0])\n",
     "\n",
     "print(\"Maximum Log Likelihood Model Instances (containing only the Gaussian centre):\\n\")\n",
@@ -928,7 +928,7 @@
     "Above, we specified each path as a list of tuples of strings. \n",
     "\n",
     "This is how the source code internally stores the path to different components of the model, but it is not \n",
-    "in-profile_1d with the PyAutoFIT API used to compose a model.\n",
+    "in-line with the PyAutoFit API used to compose a model.\n",
     "\n",
     "We can alternatively use the following API:"
    ]
@@ -954,7 +954,11 @@
     "Above, we filtered the `Samples` but asking for all parameters which included the path (\"gaussian\", \"centre\").\n",
     "\n",
     "We can alternatively filter the `Samples` object by removing all parameters with a certain path. Below, we remove\n",
-    "the Gaussian's `centre` to be left with 2 parameters; the `normalization` and `sigma`."
+    "the Gaussian's `centre`.\n",
+    "\n",
+    "Recall that the model fitted in this tutorial has two components, a `Gaussian` and an `Exponential`, and therefore\n",
+    "6 parameters in total. Removing one of them therefore leaves us with 5 parameters: the Gaussian's `normalization`\n",
+    "and `sigma`, plus all three of the Exponential's parameters."
    ]
   },
   {
@@ -972,7 +976,7 @@
     "samples = samples.without_paths([\"gaussian.centre\"])\n",
     "\n",
     "print(\n",
-    "    \"All parameters of the very first sample (containing only the Gaussian normalization and sigma).\"\n",
+    "    \"All parameters of the very first sample (with the Gaussian centre removed).\"\n",
     ")\n",
     "print(samples.parameter_lists[0])"
    ],
diff --git a/scripts/chapter_1_introduction/tutorial_5_results_and_samples.py b/scripts/chapter_1_introduction/tutorial_5_results_and_samples.py
index c6785fc..5250ce6 100644
--- a/scripts/chapter_1_introduction/tutorial_5_results_and_samples.py
+++ b/scripts/chapter_1_introduction/tutorial_5_results_and_samples.py
@@ -626,7 +626,7 @@ def model_data_from(self, xvalues: np.ndarray):
 
 samples = samples.with_paths([("gaussian", "centre")])
 
-print("All parameters of the very first sample (containing only the Gaussian centre.")
+print("All parameters of the very first sample (containing only the Gaussian centre).")
 print(samples.parameter_lists[0])
 
 print("Maximum Log Likelihood Model Instances (containing only the Gaussian centre):\n")
@@ -636,7 +636,7 @@ def model_data_from(self, xvalues: np.ndarray):
 Above, we specified each path as a list of tuples of strings. 
 
 This is how the source code internally stores the path to different components of the model, but it is not 
-in-profile_1d with the PyAutoFIT API used to compose a model.
+in-line with the PyAutoFit API used to compose a model.
 
 We can alternatively use the following API:
 """
@@ -651,7 +651,11 @@ def model_data_from(self, xvalues: np.ndarray):
 Above, we filtered the `Samples` but asking for all parameters which included the path ("gaussian", "centre").
 
 We can alternatively filter the `Samples` object by removing all parameters with a certain path. Below, we remove
-the Gaussian's `centre` to be left with 2 parameters; the `normalization` and `sigma`.
+the Gaussian's `centre`.
+
+Recall that the model fitted in this tutorial has two components, a `Gaussian` and an `Exponential`, and therefore
+6 parameters in total. Removing one of them therefore leaves us with 5 parameters: the Gaussian's `normalization`
+and `sigma`, plus all three of the Exponential's parameters.
 """
 samples = result.samples
 
@@ -664,7 +668,7 @@ def model_data_from(self, xvalues: np.ndarray):
 samples = samples.without_paths(["gaussian.centre"])
 
 print(
-    "All parameters of the very first sample (containing only the Gaussian normalization and sigma)."
+    "All parameters of the very first sample (with the Gaussian centre removed)."
 )
 print(samples.parameter_lists[0])