diff --git a/bootstrap-t.ipynb b/bootstrap-t.ipynb
index af28d86..76523f4 100644
--- a/bootstrap-t.ipynb
+++ b/bootstrap-t.ipynb
@@ -17,7 +17,10 @@
    "source": [
     "Zbiór danych          - ???\n",
     "Hipoteza zerowa       - ???\n",
-    "Hipoteza alternatywna - ???"
+    "Hipoteza alternatywna - ???\n",
+    "\n",
+    "Dla każdego z 3 testów inne\n",
+    "https://www.jmp.com/en_ch/statistics-knowledge-portal/t-test.html"
    ],
    "metadata": {
     "collapsed": false
@@ -25,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 52,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -37,12 +40,13 @@
     "import pandas as pd\n",
     "from math import sqrt\n",
     "from scipy.stats import sem\n",
-    "from scipy.stats import t"
+    "from scipy.stats import t\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 53,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -59,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 54,
    "outputs": [],
    "source": [
     "def get_t_stat(data1, data2):\n",
@@ -69,6 +73,7 @@
     "    sem2 = sem(data2)\n",
     "\n",
     "    sed = sqrt(sem1**2.0 + sem2**2.0)\n",
+    "    # To jest wzór chyba tylko dla jednego przypadku\n",
     "    return (mean1 - mean2) / sed"
    ],
    "metadata": {
@@ -80,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 55,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -103,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 56,
    "outputs": [],
    "source": [
     "def make_decision(data, columns, alpha=0.05):\n",
@@ -127,12 +132,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "outputs": [],
+   "execution_count": 69,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def draw_distribution():\n",
     "    \"\"\"Funkcja rysuje rozkład statystyki testowej\"\"\"\n",
-    "    pass"
+    "    dummy = np.random.normal(170, 10, 500)\n",
+    "    plt.hist(dummy)\n",
+    "    plt.show()\n",
+    "    pass\n",
+    "draw_distribution()"
    ],
    "metadata": {
     "collapsed": false,
@@ -143,13 +163,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 60,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "t: 6.891235313595221, df: 998, cv: 1.6463818766348755, p: 9.78683800667568e-12\n",
+      "t: 6.893215520199072, df: 998, cv: 1.6463818766348755, p: 9.657386002004387e-12\n",
       "\n",
       "Reject the null hypothesis that the means are equal.\n",
       "Reject the null hypothesis that the means are equal.\n"