diff --git a/bootstrap-t.ipynb b/bootstrap-t.ipynb
index f89a173..16a57d2 100644
--- a/bootstrap-t.ipynb
+++ b/bootstrap-t.ipynb
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 155,
    "outputs": [],
    "source": [
     "# TODO: Poprzestawiać kolejność definicji funkcji?"
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 252,
+   "execution_count": 156,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 253,
+   "execution_count": 157,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 254,
+   "execution_count": 158,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -88,19 +88,18 @@
    },
    "outputs": [],
    "source": [
-    "def t_stat_single(sample, population_mean=2):\n",
-    "    # TODO: Wywalić min, funkcja nie powinna działać dla pustej próbki\n",
-    "    # TODO: population mean nie powinien mieć defaultowego argumentu\n",
+    "def t_stat_single(sample, population_mean):\n",
     "    \"\"\"Funkcja oblicza wartość statystyki testowej dla jednej próbki\"\"\"\n",
+    "    if sample.empty:\n",
+    "        raise Exception(\"Empty sample\")\n",
     "    sample = sample[0].values.tolist()\n",
     "    sample_size = len(sample)\n",
-    "    # min is to fix near-zero values causing zero division erros\n",
-    "    return (mean(sample) - population_mean) / (stdev(sample) / min(0.00000001, sqrt(sample_size)))"
+    "    return (mean(sample) - population_mean) / (stdev(sample) / sqrt(sample_size))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 255,
+   "execution_count": 159,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -111,6 +110,8 @@
    "source": [
     "def t_stat_ind(sample_1, sample_2):\n",
     "    \"\"\"Funkcja oblicza wartość statystyki testowej dla dwóch próbek niezależnych\"\"\"\n",
+    "    if sample_1.empty or sample_2.empty:\n",
+    "        raise Exception(\"Empty sample\")\n",
     "    sample_1 = sample_1[0].values.tolist()\n",
     "    sample_2 = sample_2[0].values.tolist()\n",
     "    sed = sqrt(sem(sample_1)**2 + sem(sample_2)**2)\n",
@@ -119,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 256,
+   "execution_count": 160,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -128,36 +129,34 @@
    },
    "outputs": [],
    "source": [
-    "def t_stat_dep(sample_1, sample_2):\n",
+    "def t_stat_dep(sample_1, sample_2, mu=0):\n",
     "    \"\"\"Funkcja oblicza wartość statystyki testowej dla dwóch próbek zależnych\"\"\"\n",
-    "    # TODO: Wywalić min\n",
-    "    # TODO: Przenieść mu jako opcjonalny argument?\n",
+    "    if sample_1.empty or sample_2.empty:\n",
+    "        raise Exception(\"Empty sample\")\n",
     "    sample_1 = sample_1[0].values.tolist()\n",
     "    sample_2 = sample_2[0].values.tolist()\n",
     "    differences = [x_1 - x_2 for x_1, x_2 in zip(sample_1, sample_2)]\n",
     "    sample_size = len(sample_1)\n",
-    "    mu = 0 # The constant is zero if we want to test whether the average of the difference is significantly different.\n",
-    "    return (mean(differences) - mu) / (stdev(differences) / min(0.00000001, sqrt(sample_size)))"
+    "    return (mean(differences) - mu) / (stdev(differences) / sqrt(sample_size))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 257,
+   "execution_count": 161,
    "metadata": {},
    "outputs": [],
    "source": [
     "def df_dep(sample_1, sample_2):\n",
     "    \"\"\"Funkcja oblicza stopnie swobody dla dwóch próbek zależnych\"\"\"\n",
-    "    # TODO: Assert działa chyba tylko w trybie debugowania\n",
     "    l1, l2 = len(sample_1), len(sample_2)\n",
-    "    assert l1 == l2 \n",
-    "\n",
+    "    if l1 != l2:\n",
+    "        raise Exception(\"Samples aren't of equal length\")\n",
     "    return l1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 258,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 259,
+   "execution_count": 163,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 260,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 261,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 262,
+   "execution_count": 166,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -211,17 +210,18 @@
    },
    "outputs": [],
    "source": [
-    "def bootstrap_one_sample(sample):\n",
+    "def bootstrap_one_sample(sample, population_mean):\n",
     "    return t_test(\n",
     "        sample_1=sample,\n",
     "        df_fn=df_single,\n",
-    "        t_stat_fn=t_stat_single\n",
+    "        t_stat_fn=t_stat_single,\n",
+    "        population_mean=population_mean\n",
     "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 263,
+   "execution_count": 167,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 264,
+   "execution_count": 168,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -261,31 +261,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 265,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_t_stats(sample_1, sample_2=None, t_stat_fn=t_stat_ind):\n",
+    "def get_t_stats(sample_1, sample_2=None, t_stat_fn=t_stat_single, population_mean=None):\n",
     "    \"\"\"Funkcja oblicza listę statystyk testowych dla każdej próbki bootstrapowej wybranej na podstawie danych sample_1 i sample_2\"\"\"\n",
     "    t_stat_list = []\n",
     "\n",
-    "    # Separate case for single tests\n",
-    "    if sample_2 is None:\n",
+    "    # One sample test\n",
+    "    if t_stat_fn==t_stat_single:\n",
+    "        if not population_mean:\n",
+    "            raise Exception(\"population_mean not provided\")\n",
     "        for bootstrap in generate_bootstraps(sample_1):\n",
-    "            stat = t_stat_fn(bootstrap)\n",
+    "            stat = t_stat_fn(bootstrap, population_mean)\n",
     "            t_stat_list.append(stat)\n",
     "        return t_stat_list\n",
-    "    \n",
+    "\n",
+    "    # Two sample test\n",
     "    for bootstrap_1, bootstrap_2 in zip(generate_bootstraps(sample_1), generate_bootstraps(sample_2)):\n",
     "        stat = t_stat_fn(bootstrap_1, bootstrap_2)\n",
     "        t_stat_list.append(stat)\n",
-    "    \n",
     "    return t_stat_list"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 266,
+   "execution_count": 170,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -293,34 +295,34 @@
    },
    "outputs": [],
    "source": [
-    "def t_test(sample_1, sample_2=None, df_fn=df_ind, t_stat_fn=t_stat_ind, alpha=0.05):\n",
+    "def t_test(sample_1, sample_2=None, df_fn=df_single, t_stat_fn=t_stat_single, population_mean=None, alpha=0.05):\n",
     "    \"\"\"\n",
     "    Funkcja przeprowadza test T-studenta dla dwóch zmiennych.\n",
     "    liczba kolumn wynosi 1, test jest przeprowadzany dla jednej zmiennej.\n",
     "    @param df_fn - funkcja obliczająca stopnie swobody\n",
     "    @param t_stat_fn - funkcja obliczająca statystykę T\n",
     "    \"\"\"\n",
-    "    t_stat_list = get_t_stats(sample_1, sample_2, t_stat_fn)\n",
+    "    t_stat_list = get_t_stats(sample_1, sample_2, t_stat_fn, population_mean=population_mean)\n",
     "    t_stat_sum = sum(t_stat_list)\n",
     "\n",
     "    data_size = sample_1.shape[0]\n",
     "\n",
     "    t_stat = t_stat_sum / data_size\n",
+    "    # TODO: dolna i górna opcja dają inne wyniki z jakiegoś powodu (???)\n",
+    "    t_stat = mean(t_stat_list)\n",
     "\n",
-    "    df = 0.0\n",
     "    if sample_2 is None:\n",
     "        df = df_fn(sample_1)\n",
     "    else:\n",
     "        df = df_fn(sample_1, sample_2)\n",
     "    cv = calculate_cv(df, alpha)\n",
     "    p = calculate_p(t_stat, df)\n",
-    "    \n",
     "    return t_stat, df, cv, p, t_stat_list"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 267,
+   "execution_count": 171,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -332,7 +334,7 @@
     "def draw_distribution(stats):\n",
     "    \"\"\"\n",
     "    Funkcja rysuje rozkład statystyki testowej\n",
-    "    stats: lista statystyk testowych\n",
+    "    @param stats: lista statystyk testowych\n",
     "    \"\"\"\n",
     "    plt.hist(stats)\n",
     "    plt.xlabel('Test statistic value')\n",
@@ -342,23 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 268,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def make_decision(data, columns):\n",
-    "    # TODO: Potrzebna ta funkcja w ogóle? Decyzja jest zależna od wybranych hipotez chyba.\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 269,
+   "execution_count": 172,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -371,7 +357,7 @@
      "output_type": "stream",
      "text": [
       "Statystyka testowa dla jednej próby:\n",
-      "6.324555320336758e-09 - z naszej funkcji\n",
+      "1.414213562373095 - z naszej funkcji\n",
       "[1.41421356] - z gotowej biblioteki\n",
       "\n",
       "Statystyka testowa dla dwóch prób niezależnych:\n",
@@ -379,7 +365,7 @@
       "[-3.] - z gotowej biblioteki\n",
       "\n",
       "Statystyka testowa dla dwóch prób zależnych:\n",
-      "-7.302967433402215e-09 - z naszej funkcji\n",
+      "-1.6329931618554525 - z naszej funkcji\n",
       "[-1.63299316] - z gotowej biblioteki\n",
       "\n"
      ]
@@ -412,28 +398,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 270,
+   "execution_count": 173,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Statystyki dla jednej próby:\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "t_stat_single() missing 1 required positional argument: 'population_mean'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
-      "Input \u001B[1;32mIn [270]\u001B[0m, in \u001B[0;36m<cell line: 7>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      4\u001B[0m     \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mt: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mt_stat\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, df: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mdf\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, cv: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcv\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, p: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mp\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m      6\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mStatystyki dla jednej próby:\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m----> 7\u001B[0m t_stat, df, cv, p, _ \u001B[38;5;241m=\u001B[39m \u001B[43mbootstrap_one_sample\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdummy\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m      8\u001B[0m pretty_print_full_stats(t_stat, df, cv, p)\n\u001B[0;32m     10\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mStatystyki dla dwóch prób zależnych:\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
-      "Input \u001B[1;32mIn [262]\u001B[0m, in \u001B[0;36mbootstrap_one_sample\u001B[1;34m(sample)\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mbootstrap_one_sample\u001B[39m(sample):\n\u001B[1;32m----> 2\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mt_test\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m      3\u001B[0m \u001B[43m        \u001B[49m\u001B[43msample_1\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msample\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      4\u001B[0m \u001B[43m        \u001B[49m\u001B[43mdf_fn\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdf_single\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      5\u001B[0m \u001B[43m        \u001B[49m\u001B[43mt_stat_fn\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mt_stat_single\u001B[49m\n\u001B[0;32m      6\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
-      "Input \u001B[1;32mIn [266]\u001B[0m, in \u001B[0;36mt_test\u001B[1;34m(sample_1, sample_2, df_fn, t_stat_fn, alpha)\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mt_test\u001B[39m(sample_1, sample_2\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, df_fn\u001B[38;5;241m=\u001B[39mdf_ind, t_stat_fn\u001B[38;5;241m=\u001B[39mt_stat_ind, alpha\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0.05\u001B[39m):\n\u001B[0;32m      2\u001B[0m     \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m      3\u001B[0m \u001B[38;5;124;03m    Funkcja przeprowadza test T-studenta dla dwóch zmiennych.\u001B[39;00m\n\u001B[0;32m      4\u001B[0m \u001B[38;5;124;03m    liczba kolumn wynosi 1, test jest przeprowadzany dla jednej zmiennej.\u001B[39;00m\n\u001B[0;32m      5\u001B[0m \u001B[38;5;124;03m    @param df_fn - funkcja obliczająca stopnie swobody\u001B[39;00m\n\u001B[0;32m      6\u001B[0m \u001B[38;5;124;03m    @param t_stat_fn - funkcja obliczająca statystykę T\u001B[39;00m\n\u001B[0;32m      7\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m----> 8\u001B[0m     t_stat_list \u001B[38;5;241m=\u001B[39m \u001B[43mget_t_stats\u001B[49m\u001B[43m(\u001B[49m\u001B[43msample_1\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msample_2\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mt_stat_fn\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m      9\u001B[0m     t_stat_sum \u001B[38;5;241m=\u001B[39m \u001B[38;5;28msum\u001B[39m(t_stat_list)\n\u001B[0;32m     11\u001B[0m     data_size \u001B[38;5;241m=\u001B[39m sample_1\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m]\n",
-      "Input \u001B[1;32mIn [265]\u001B[0m, in \u001B[0;36mget_t_stats\u001B[1;34m(sample_1, sample_2, t_stat_fn)\u001B[0m\n\u001B[0;32m      6\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m sample_2 \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m      7\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m bootstrap \u001B[38;5;129;01min\u001B[39;00m generate_bootstraps(sample_1):\n\u001B[1;32m----> 8\u001B[0m         stat \u001B[38;5;241m=\u001B[39m \u001B[43mt_stat_fn\u001B[49m\u001B[43m(\u001B[49m\u001B[43mbootstrap\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m      9\u001B[0m         t_stat_list\u001B[38;5;241m.\u001B[39mappend(stat)\n\u001B[0;32m     10\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m t_stat_list\n",
-      "\u001B[1;31mTypeError\u001B[0m: t_stat_single() missing 1 required positional argument: 'population_mean'"
+      "Statystyki dla jednej próby:\n",
+      "t: 1.8524997668616348, df: 5, cv: 2.015048372669157, p: 0.12315232406912302\n",
+      "\n",
+      "Statystyki dla dwóch prób zależnych:\n",
+      "t: 3.166992562129946, df: 5, cv: 2.015048372669157, p: 0.02489883191814224\n",
+      "\n",
+      "Statystyki dla dwóch prób niezależnych:\n",
+      "t: 3.0429202631473986, df: 8, cv: 1.8595480375228421, p: 0.015992147409949586\n",
+      "\n"
      ]
     }
    ],
@@ -444,7 +424,7 @@
     "    print(f't: {t_stat}, df: {df}, cv: {cv}, p: {p}\\n')\n",
     "\n",
     "print('Statystyki dla jednej próby:')\n",
-    "t_stat, df, cv, p, _ = bootstrap_one_sample(dummy)\n",
+    "t_stat, df, cv, p, _ = bootstrap_one_sample(dummy, 2)\n",
     "pretty_print_full_stats(t_stat, df, cv, p)\n",
     "\n",
     "print('Statystyki dla dwóch prób zależnych:')\n",
@@ -458,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 174,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -468,7 +448,7 @@
    "outputs": [],
    "source": [
     "dataset = pd.read_csv('experiment_data.csv')\n",
-    "make_decision(dataset, ['Weight', 'Age'])"
+    "#make_decision(dataset, ['Weight', 'Age'])"
    ]
   }
  ],