diff --git a/lab-hypothesis-testing.ipynb b/lab-hypothesis-testing.ipynb
index 0cc26d5..2f741bc 100644
--- a/lab-hypothesis-testing.ipynb
+++ b/lab-hypothesis-testing.ipynb
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -278,7 +278,7 @@
        "[800 rows x 11 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -297,11 +297,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(4.097528915272702), np.float64(0.00010181538122353851))"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 1. Filter groups (using 'Type 1' et 'Type 2')\n",
+    "hp_dragon = df[(df['Type 1'] == 'Dragon') | (df['Type 2'] == 'Dragon')]['HP']\n",
+    "hp_grass = df[(df['Type 1'] == 'Grass') | (df['Type 2'] == 'Grass')]['HP']\n",
+    "\n",
+    "\n",
+    "# 2. Run the T-test (assuming the ttest_ind function from scipy.stats)\n",
+    "# The parameter 'equal_var=False' is often preferred when the sample sizes are different.\n",
+    "from scipy import stats\n",
+    "t_stat, p_value_two_sided = stats.ttest_ind(hp_dragon, hp_grass, equal_var=False)\n",
+    "t_stat, p_value_two_sided \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(5.0907690611769255e-05)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "# 3. Convert the p-value to a one-sided analysis\n",
+    "p_value_one_sided = p_value_two_sided / 2\n",
+    "\n",
+    "p_value_one_sided "
    ]
   },
   {
@@ -313,11 +357,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 8.98137048, 10.43813354,  7.63707816, 13.41744998, 10.01569661,\n",
+       "        11.47504445]),\n",
+       " array([1.00269117e-13, 2.52037245e-16, 4.82699849e-11, 1.55146141e-21,\n",
+       "        2.29493279e-15, 1.04901631e-18]))"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "#code herestats_legendary = df[df['Legendary'] == True][['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']]\n",
+    "\n",
+    "# Filter all rows where 'Legendary' is True\n",
+    "stats_legendary = df[df['Legendary'] == True][['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']]\n",
+    "# Filter all rows where 'Legendary' is False\n",
+    "stats_non_legendary = df[df['Legendary'] == False][['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']]\n",
+    "\n",
+    "# The list of stats to test\n",
+    "stat_columns = ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']\n",
+    "\n",
+    "from scipy import stats\n",
+    "t_stat, p_value_two_sided = stats.ttest_ind(stats_legendary,stats_non_legendary, equal_var=False)\n",
+    "t_stat, p_value_two_sided \n",
+    "\n"
    ]
   },
   {
@@ -337,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -453,7 +524,7 @@
        "4       624.0       262.0         1.9250             65500.0  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -498,7 +569,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -512,7 +583,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,