diff --git a/bootstrap-t.ipynb b/bootstrap-t.ipynb
index 76523f4..c62c3ed 100644
--- a/bootstrap-t.ipynb
+++ b/bootstrap-t.ipynb
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 15,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -41,12 +41,13 @@
     "from math import sqrt\n",
     "from scipy.stats import sem\n",
     "from scipy.stats import t\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "from statistics import mean, stdev"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 16,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -63,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 17,
    "outputs": [],
    "source": [
     "def get_t_stat(data1, data2):\n",
@@ -85,7 +86,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "def  t_stat_single(sample, population_mean):\n",
+    "    \"\"\"Funkcja oblicza wartość statystyki testowej dla jednej próbki\"\"\"\n",
+    "    sample_mean = mean(sample)\n",
+    "    sample_std = stdev(sample)\n",
+    "    sample_size = len(sample)\n",
+    "    return (sample_mean - population_mean) / (sample_std / sqrt(sample_size))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "def t_stat_indept():\n",
+    "    pass"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "def t_stat_dep():\n",
+    "    pass"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -108,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 19,
    "outputs": [],
    "source": [
     "def make_decision(data, columns, alpha=0.05):\n",
@@ -132,12 +182,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 20,
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": "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\n"
+      "image/png": "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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -163,13 +213,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 21,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "t: 6.893215520199072, df: 998, cv: 1.6463818766348755, p: 9.657386002004387e-12\n",
+      "t: 6.940510630195086, df: 998, cv: 1.6463818766348755, p: 7.02371494298859e-12\n",
       "\n",
       "Reject the null hypothesis that the means are equal.\n",
       "Reject the null hypothesis that the means are equal.\n"