diff --git a/02_activities/assignments/a1_sampling_and_reproducibility.ipynb b/02_activities/assignments/a1_sampling_and_reproducibility.ipynb
index 11852458..e9786172 100644
--- a/02_activities/assignments/a1_sampling_and_reproducibility.ipynb
+++ b/02_activities/assignments/a1_sampling_and_reproducibility.ipynb
@@ -16,7 +16,37 @@
    "cell_type": "markdown",
    "id": "4ea73db3",
    "metadata": {},
-   "source": []
+   "source": [
+    "I am splitting my answer into 4 steps with detailed answers for each subsection: \n",
+    "\n",
+    "Step 1: Setting Up the Dataframe \n",
+    "Function Used: pd.DataFrame\n",
+    "Sample Size: 1000 individuals\n",
+    "Sampling Frame: Everyone in attendance (whole population) of the two events, brunches and weddings \n",
+    "Procedure: The first several lines of code following all of the imports are to set up a dataframe of what I think is people that attended the wedding and people that attended a brunch. The script creates n= 1000, with 800 individuals at the brunch and 200 at the wedding \n",
+    "Underlying Distribution: deterministic allocation (200 weddings, 800 brunches) \n",
+    "\n",
+    "\n",
+    "Step 2: Infecting a Random Subset of Individuals\n",
+    "Functions used: np.random.choice\n",
+    "Sample Size: Attack RATE = 0.10, so 0.10 *1000 = 100 individuals sampled (or infected)\n",
+    "Sampling Frame: Everyone in attendance (whole population) of the two events could have been infected. Although everyone in the data frame initially (that is everyone who went to the wedding and brunch is in your sampling frame) has an equal chance of being sampled, this is sampling without replacement, as your replace argument is set to FALSE. This makes sense, given the fact that if someone is infected, there is no point in reinfecting them. \n",
+    "Underlying distribution: Technically uniform, and could vary given that the code does not specify a specific proportion in wedding/brunch infection. \n",
+    "\n",
+    "\n",
+    "Step 3: Primary Contact Tracing \n",
+    "Functions used: np.random.rand\n",
+    "Sample Size/Sampling Frame: Every 100 infected individual gets sampled, given that we are assigning a 0/1 to each of them. The 0.20 probability helps inform us which ones are traced and which ones are not! \n",
+    "Within your primary tracing, your sampling frame is people already infected, and so you are trying to decide which one of those you want to trace. You have zoomed in, moving away from the entire dataframe of people. In this case, your sample is likely much smaller as well. \n",
+    "\n",
+    "Step 4: Secondary contact tracing \n",
+    "Functions used: value_counts() - used to count event types!\n",
+    "Sample Size/Sampling: our sampling are all of those that got traced. I estimate this sample size to be ~20, though of course 0.2 is just a probability, so depending on our run, we could have 18 or 19 or 21 people traced! \n",
+    "\n",
+    "The code following that is not so much sampling, but just calculating propotions. \n",
+    "\n",
+    "Finally in the end, you created 1000 simulations of this. In terms of the 1000 simulations, you end up getting a red \"traced\" graph that approximates a normal distribution with a left tail, while the blue \"infect\" graph has less of a tail approximating a normal distribution. When you run 1000, you also notice that the red graph approximates the blue one. I can only imagine that if you run 10,000 simulations, this approximation would become closer. "
+   ]
   },
   {
    "cell_type": "markdown",
@@ -30,7 +60,9 @@
    "cell_type": "markdown",
    "id": "4cf5d993",
    "metadata": {},
-   "source": []
+   "source": [
+    "I tried running simulation of 10, 100, and 1000. In the 10, it did not present any coherent distribution, but possible a square or uniform-type distribution given the small simulation size. In the 100 simulations, the graph started to look like the 1000. You will notice particularly in the traced \"red\" part that there is a distribution approximating a normal distribution with a skew and tail to the left. The few results in this tail essentially saw, the lower the frequency, the lower the proportion of cases! As you increase the \"n\" number of simulations, red becomes a closer approximation of the blue. The traced v. infected in other words. Lastly, I will mention that the results are not quite repoducible, because there is no seed set! So each time you run it, the mean of the histograms may shift, or the spreadness and kurtosis of the distributions can vary! Edit: In the first pull request, the answer got cut off "
+   ]
   },
   {
    "cell_type": "markdown",
@@ -44,7 +76,9 @@
    "cell_type": "markdown",
    "id": "77613cc3",
    "metadata": {},
-   "source": []
+   "source": [
+    "I simply added \"np.random.seed(123)\". By adding the seed, it ensures that each time we are running a similar simulation producing similar results. When i run the code multiple times, it presents the same output! Edited: For some reason, this answer did not show up on the first pull request, even though I wrote it! "
+   ]
   },
   {
    "cell_type": "markdown",
@@ -56,10 +90,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "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",
@@ -130,8 +175,9 @@
     "\n",
     "  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",
+    "# Run the simulation 100 times\n",
+    "np.random.seed(123)  # For reproducibility\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",
@@ -207,7 +253,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,