diff --git a/.github/workflows/deploy-jupyter-book.yml b/.github/workflows/deploy-jupyter-book.yml
index c5a74a7..f72315b 100644
--- a/.github/workflows/deploy-jupyter-book.yml
+++ b/.github/workflows/deploy-jupyter-book.yml
@@ -35,6 +35,11 @@ jobs:
       run: |
         python -m pip list
 
+    - name: Set track_progress=False for notebooks
+      run: |
+        git config --global --add safe.directory /__w/pyhf-tutorial/pyhf-tutorial
+        git grep --name-only "track_progress=True" book | xargs sed --in-place 's/track_progress=True/track_progress=False/g'
+
     - name: Build the book
       run: |
         jupyter-book build book/
diff --git a/book/Toys.ipynb b/book/Toys.ipynb
index bba90cc..8ed4bc6 100644
--- a/book/Toys.ipynb
+++ b/book/Toys.ipynb
@@ -64,15 +64,6 @@
     "So the question is, does the asymptotic approximation hold in this example? The standard assumption is that you have enough \"statistics\" (meaning enough events) to use the large-N approximation. So let's use the toy-based calculator instead and compute the same values as above and see if they match in the asymptotic case (we certainly hope they mostly do here!)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "For readability in the Jupyter Book the `hypotest` has `track_progress=False`. If you're running this notebook yourself you might want to set `track_progress=True` to enable the progress bar.\n",
-    "```"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -87,7 +78,7 @@
     "    return_expected_set=True,\n",
     "    calctype=\"toybased\",\n",
     "    ntoys=1_000,\n",
-    "    track_progress=False,\n",
+    "    track_progress=True,\n",
     ")\n",
     "print(f\"      Observed CLs: {CLs_obs:.4f}\")\n",
     "for expected_value, n_sigma in zip(CLs_exp, np.arange(-2, 3)):\n",
@@ -168,7 +159,7 @@
     "    return_expected_set=True,\n",
     "    calctype=\"toybased\",\n",
     "    ntoys=5_000,\n",
-    "    track_progress=False,\n",
+    "    track_progress=True,\n",
     ")\n",
     "print(f\"      Observed CLs: {CLs_obs:.4f}\")\n",
     "for expected_value, n_sigma in zip(CLs_exp, np.arange(-2, 3)):\n",
diff --git a/book/UsingCalculators.ipynb b/book/UsingCalculators.ipynb
index 9dd266d..a35aae0 100644
--- a/book/UsingCalculators.ipynb
+++ b/book/UsingCalculators.ipynb
@@ -224,15 +224,6 @@
     "Let's create a toy-based calculator and \"throw\" 500 toys."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "For readability in the Jupyter Book the `ToyCalculator` has `track_progress=False`. If you're running this notebook yourself you might want to set `track_progress=True` to enable the progress bar.\n",
-    "```"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -240,7 +231,7 @@
    "outputs": [],
    "source": [
     "toy_calc = pyhf.infer.calculators.ToyCalculator(\n",
-    "    data, model, test_stat=\"qtilde\", ntoys=500, track_progress=False\n",
+    "    data, model, test_stat=\"qtilde\", ntoys=500, track_progress=True\n",
     ")"
    ]
   },