Adding an example of performing t-test as in the textbook for whether…

… being physically active impacts BMI

notebooks/08-HypothesisTesting.ipynb

@@ -118,6 +118,70 @@
         "#"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Performing t-test with Python\n",
+        "Let's draw a sample of 250 participants from the \"population\" who participated the NHANES study\n"
+      ],
+      "metadata": {
+        "id": "Jb361YaoZQG4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "! pip install nhanes\n",
+        "from nhanes.load import load_NHANES_data\n",
+        "nhanes_data = load_NHANES_data()\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "oYoivOQeZ2nt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import seaborn as sns\n",
+        "sample_size = 250\n",
+        "nhanes_data['PhysActive'] = np.logical_or(nhanes_data['VigorousRecreationalActivities'], nhanes_data['ModerateRecreationalActivities'])\n",
+        "print('Unique values in PhysActive:',nhanes_data['PhysActive'].unique())\n",
+        "\n",
+        "sample = nhanes_data.dropna(subset=['PhysActive', 'BodyMassIndexKgm2']).sample(sample_size)\n",
+        "sns.boxplot(data=sample, x=\"PhysActive\", y=\"BodyMassIndexKgm2\")"
+      ],
+      "metadata": {
+        "id": "BjiYATqLc_lA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from scipy.stats import ttest_ind\n",
+        "# By default, ttest_ind assumes equal variance of the two samples\n",
+        "print('assuming equal variance of the two population:')\n",
+        "t, p = ttest_ind(sample.query('PhysActive==1.0')['BodyMassIndexKgm2'], sample.query('PhysActive==0.0')['BodyMassIndexKgm2'])\n",
+        "print('t-statistic:', t)\n",
+        "print('p-value:', p)\n",
+        "\n",
+        "# If we don't make the assumption, the result may be slightly different:\n",
+        "print('without assuming equal variance of the two populations:')\n",
+        "t, p = ttest_ind(sample.query('PhysActive==1.0')['BodyMassIndexKgm2'], sample.query('PhysActive==0.0')['BodyMassIndexKgm2'], equal_var=False)\n",
+        "print('t-statistic:', t)\n",
+        "print('p-value:', p)\n"
+      ],
+      "metadata": {
+        "id": "gSmTuPZmlqsn"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "metadata": {