diff --git a/02_activities/assignments/a1_sampling_and_reproducibility.ipynb b/02_activities/assignments/a1_sampling_and_reproducibility.ipynb
index 11852458..e84aa41c 100644
--- a/02_activities/assignments/a1_sampling_and_reproducibility.ipynb
+++ b/02_activities/assignments/a1_sampling_and_reproducibility.ipynb
@@ -16,7 +16,22 @@
    "cell_type": "markdown",
    "id": "4ea73db3",
    "metadata": {},
-   "source": []
+   "source": [
+    "The model includes:\n",
+    "\n",
+    "Stage 1: Creating a fixed population of 1,000 individuals, with 200 attending weddings and 800 attending brunches.\n",
+    "\n",
+    "Stage 2: Randomly selecting 10% percent of the whole population (independent of event type) and marking them as infected using np.random.choice. This stage implements simple random sampling without replacement from a uniform distribution over the population. The sampling frame is the whole population (1000), and the outcome sample size is 100.\n",
+    "\n",
+    "Stage 3: Each infected individual (independent of event types) is independently assigned a 20% chance of being traced using np.random.rand compared to 0.2, which implements Bernoulli sampling with a Bernoulli(p=0.2) distribution. The sampling frame is all infected individuals, and the outcome sample size is not fixed.\n",
+    "\n",
+    "Stage 4: Counting the number of traced individuals from each event type.\n",
+    "\n",
+    "Stage 5: Selecting events with at least 2 traced individuals, and marking all infected individuals in those events as traced. This is a non-probability sampling without any randomness.\n",
+    "\n",
+    "Stage 6: Counting Infected and traced individuals by event type, calculating proportions, and finally plotting the results.\n",
+    "\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -30,7 +45,9 @@
    "cell_type": "markdown",
    "id": "4cf5d993",
    "metadata": {},
-   "source": []
+   "source": [
+    "The 10-times simulation is strongly variable, indicating very bad reproducibility. The 100-times simulation, by contrast, shows relatively small fluctuations across simulations, indicating a more stable, reproducible result."
+   ]
   },
   {
    "cell_type": "markdown",
@@ -44,7 +61,9 @@
    "cell_type": "markdown",
    "id": "77613cc3",
    "metadata": {},
-   "source": []
+   "source": [
+    "I included a random seed before the simulation. The random seed code lock the randomness, i.e., initializing the random number generator to a fixed starting state, so that all subsequent random draws follow the same deterministic sequence, without undermining the simulation procedure itself."
+   ]
   },
   {
    "cell_type": "markdown",
@@ -56,10 +75,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "ab8587a0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Import necessary libraries\n",
     "import pandas as pd\n",
@@ -80,6 +110,10 @@
     "TRACE_SUCCESS = 0.20\n",
     "SECONDARY_TRACE_THRESHOLD = 2\n",
     "\n",
+    "# Include a random seed to ensure the reproducibility of the simulation\n",
+    "np.random.seed(25)\n",
+    "\n",
+    "\n",
     "def simulate_event(m):\n",
     "  \"\"\"\n",
     "  Simulates the infection and tracing process for a series of events.\n",
@@ -131,7 +165,7 @@
     "  return p_wedding_infections, p_wedding_traces\n",
     "\n",
     "# Run the simulation 1000 times\n",
-    "results = [simulate_event(m) for m in range(1000)]\n",
+    "results = [simulate_event(m) for m in range(100)]\n",
     "props_df = pd.DataFrame(results, columns=[\"Infections\", \"Traces\"])\n",
     "\n",
     "# Plotting the results\n",
@@ -193,7 +227,7 @@
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  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "sampling-env",
    "language": "python",
    "name": "python3"
   },
@@ -207,7 +241,7 @@
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    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.11.13"
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  "nbformat": 4,
diff --git a/uv.lock b/uv.lock
index 467e0b9c..1745642f 100644
--- a/uv.lock
+++ b/uv.lock
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