diff --git a/bootstrap-t.ipynb b/bootstrap-t.ipynb
index 8f5b40c..feee38d 100644
--- a/bootstrap-t.ipynb
+++ b/bootstrap-t.ipynb
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 68,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -35,18 +35,14 @@
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from math import sqrt\n",
-    "from scipy import stats\n",
-    "from scipy.stats import sem\n",
-    "from scipy.stats import t\n",
     "import matplotlib.pyplot as plt\n",
-    "from statistics import mean, stdev\n",
-    "from scipy.stats import ttest_ind, ttest_1samp, ttest_rel"
+    "from enum import Enum\n",
+    "from scipy.stats import ttest_ind, ttest_1samp, ttest_rel, shapiro"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,29 +51,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def calculate_p(t_stat, df):\n",
-    "    \"\"\"Funkcja oblicza wartość *p* na podstawie statystyki testowej i stopni swobody\"\"\"\n",
-    "    return (1.0 - t.cdf(abs(t_stat), df)) * 2.0"
+    "class Alternatives(Enum):\n",
+    "    LESS = 'less'\n",
+    "    GREATER = 'greater'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def calculate_cv(df, alpha=0.05):\n",
-    "    \"\"\"Funkcja oblicza wartość krytyczną (critical value)\"\"\"\n",
-    "    return t.ppf(1.0 - alpha, df)"
+    "def calculate_t_difference(t_stat_sample, t_stat_list, alternative):\n",
+    "    \"\"\"\n",
+    "    Funkcja oblicza procent statystyk testowych powstałych z prób bootstrapowych, \n",
+    "    które róznią się od statystyki testowej powstałej ze zbioru według hipotezy alternatywnej.\n",
+    "    \"\"\"\n",
+    "    all_stats = len(t_stat_list)\n",
+    "    stats_different_count = 0\n",
+    "    for t_stat_boot in t_stat_list:\n",
+    "        if alternative is Alternatives.LESS and t_stat_boot < t_stat_sample:\n",
+    "            stats_different_count += 1 \n",
+    "        elif alternative is Alternatives.GREATER and t_stat_boot > t_stat_sample:\n",
+    "            stats_different_count += 1\n",
+    "    return stats_different_count / all_stats"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 72,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -85,57 +91,112 @@
    },
    "outputs": [],
    "source": [
-    "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",
+    "def t_test_1_samp(sample_1, population_mean=None, alternative=Alternatives.LESS):\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",
+    "    Funkcja przeprowadza test T-studenta dla jednej zmiennej.\n",
     "    \"\"\"\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",
+    "    t_stat_from_sample, _ = ttest_1samp(a=sample_1, popmean=population_mean, alternative=alternative.value)\n",
+    "    t_stat_list = get_t_stats(sample_1, t_stat_fn=ttest_1samp, alternative=alternative, population_mean=population_mean)\n",
     "\n",
-    "    data_size = sample_1.shape[0]\n",
+    "    p = calculate_t_difference(t_stat_from_sample, t_stat_list, alternative)\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",
-    "    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",
-    "    return t_stat, df, cv, p, t_stat_list"
+    "    return p, t_stat_from_sample, t_stat_list"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_t_stats(sample_1, sample_2=None, t_stat_fn=t_stat_single, population_mean=None):\n",
+    "def t_test_ind(sample_1, sample_2, alternative=Alternatives.LESS):\n",
+    "    \"\"\"\n",
+    "    Funkcja przeprowadza test T-studenta dla dwóch zmiennych niezależnych.\n",
+    "    \"\"\"\n",
+    "    t_stat_from_sample, _ = ttest_ind(sample_1, sample_2, alternative=alternative.value)\n",
+    "    t_stat_list = get_t_stats(sample_1, sample_2, alternative=alternative, t_stat_fn=ttest_ind)\n",
+    "\n",
+    "    p = calculate_t_difference(t_stat_from_sample, t_stat_list, alternative)\n",
+    "\n",
+    "    return p, t_stat_from_sample, t_stat_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def t_test_dep(sample_1, sample_2, alternative=Alternatives.LESS):\n",
+    "    \"\"\"\n",
+    "    Funkcja przeprowadza test T-studenta dla dwóch zmiennych zależnych.\n",
+    "    \"\"\"\n",
+    "    t_stat_list = get_t_stats(sample_1, sample_2, alternative=alternative, t_stat_fn=ttest_rel)\n",
+    "    t_stat_from_sample, _ = ttest_rel(sample_1, sample_2, alternative=alternative.value)\n",
+    "\n",
+    "    p = calculate_t_difference(t_stat_from_sample, t_stat_list, alternative)\n",
+    "\n",
+    "    return p, t_stat_from_sample, t_stat_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_t_stats(sample_1, sample_2=None, t_stat_fn=ttest_1samp, alternative=Alternatives.LESS, 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",
     "    # One sample test\n",
-    "    if t_stat_fn==t_stat_single:\n",
+    "    if t_stat_fn is ttest_1samp and sample_2 is None:\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, population_mean)\n",
+    "            stat, _ = t_stat_fn(bootstrap, population_mean, alternative=alternative.value)\n",
     "            t_stat_list.append(stat)\n",
     "        return t_stat_list\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",
+    "    for bootstrap_sample in generate_bootstraps(pd.concat((sample_1, sample_2), ignore_index=True)):\n",
+    "        bootstrap_1 = bootstrap_sample.iloc[: len(bootstrap_sample) // 2]\n",
+    "        bootstrap_2 = bootstrap_sample.iloc[len(bootstrap_sample) // 2 :]\n",
+    "        stat, _ = t_stat_fn(bootstrap_1, bootstrap_2, alternative=alternative.value)\n",
     "        t_stat_list.append(stat)\n",
     "    return t_stat_list"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pretty_print_test(p, t_stat_from_sample, t_stat_list, thesis, alternative, max_print=5):\n",
+    "    print('Wyniki bootstrapowej wersji testu T-studenta')\n",
+    "    print()\n",
+    "    print(f'Hipoteza: {thesis}')\n",
+    "    if alternative is Alternatives.LESS:\n",
+    "        print(f'Hipoteza alternatywna: średnia jest mniejsza')\n",
+    "    else:\n",
+    "        print(f'Hipoteza alternatywna: średnia jest większa')\n",
+    "    print()\n",
+    "    print(f'p: {p}')\n",
+    "    print(f'Wartość statystyki testowej z próby: {t_stat_from_sample}')\n",
+    "    print(f'Wartości statystyk z prób boostrapowych:')\n",
+    "\n",
+    "    t_stat_list_len = len(t_stat_list)\n",
+    "    for i in range(min(max_print, t_stat_list_len)):\n",
+    "        print(f'{t_stat_list[i]}, ', end='')\n",
+    "    if max_print < t_stat_list_len:\n",
+    "        remaining = t_stat_list_len - max_print\n",
+    "        print(f'... (i {remaining} pozostałych)')\n",
+    "\n",
+    "    print()\n",
+    "    print()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -147,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -164,7 +225,7 @@
    "source": [
     "ALPHA = 0.05\n",
     "female_heights = dataset['Female height'].to_numpy()\n",
-    "shapiro_test = stats.shapiro(female_heights)\n",
+    "shapiro_test = shapiro(female_heights)\n",
     "\n",
     "if shapiro_test.pvalue >  ALPHA:\n",
     "    print(\"Female height: Dane mają rozkład normalny.\")\n",
@@ -172,7 +233,7 @@
     "    print(\"Female height: Dane nie mają rozkładu normalnego.\")\n",
     "\n",
     "male_heights = dataset['Male height'].to_numpy()\n",
-    "shapiro_test = stats.shapiro(male_heights)\n",
+    "shapiro_test = shapiro(male_heights)\n",
     "\n",
     "if shapiro_test.pvalue >  ALPHA:\n",
     "    print(\"Male height: Dane mają rozkład normalny.\")\n",
@@ -180,7 +241,7 @@
     "    print(\"Male height: Dane nie mają rozkładu normalnego.\")\n",
     "\n",
     "weights_before = dataset['Weight before'].to_numpy()\n",
-    "shapiro_test = stats.shapiro(weights_before)\n",
+    "shapiro_test = shapiro(weights_before)\n",
     "\n",
     "if shapiro_test.pvalue >  ALPHA:\n",
     "    print(\"Weight before: Dane mają rozkład normalny.\")\n",
@@ -188,7 +249,7 @@
     "    print(\"Weight before: Dane nie mają rozkładu normalnego.\")\n",
     "\n",
     "weights_after = dataset['Weight after'].to_numpy()\n",
-    "shapiro_test = stats.shapiro(weights_after)\n",
+    "shapiro_test = shapiro(weights_after)\n",
     "\n",
     "if shapiro_test.pvalue >  ALPHA:\n",
     "    print(\"Weight after: Dane mają rozkład normalny.\")\n",
@@ -211,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 78,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -239,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 79,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -248,45 +309,16 @@
    },
    "outputs": [],
    "source": [
-    "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['Height'].values.tolist()\n",
-    "    sample_size = len(sample)\n",
-    "    return (mean(sample) - population_mean) / (stdev(sample) / sqrt(sample_size))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def df_single(sample_1):\n",
-    "    \"\"\"Funkcja oblicza stopnie swobody dla jednej próbki\"\"\"\n",
-    "    # TODO: I have no clue what to return from here\n",
-    "    return len(sample_1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def bootstrap_one_sample(sample, population_mean):\n",
-    "    return t_test(\n",
+    "def bootstrap_one_sample(sample, population_mean, alternative=Alternatives.LESS):\n",
+    "    p, t, ts = t_test_1_samp(\n",
     "        sample_1=sample,\n",
-    "        df_fn=df_single,\n",
-    "        t_stat_fn=t_stat_single,\n",
-    "        population_mean=population_mean\n",
-    "    )"
+    "        population_mean=population_mean,\n",
+    "        alternative=alternative,\n",
+    "    )\n",
+    "    \n",
+    "    pretty_print_test(p, t, ts, f'średnia jest równa {population_mean}', alternative)\n",
+    "    print()\n",
+    "    return p, t, ts"
    ]
   },
   {
@@ -298,7 +330,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dummy = pd.DataFrame([1, 2, 3, 4, 5])\n",
+    "dummy2 = pd.DataFrame([4, 5, 6, 7, 8])\n",
+    "dummy3 = pd.DataFrame([1, 3 , 3, 4, 6])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -310,7 +353,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "t: 6.854929920812628, df: 500, cv: 1.6479068539295045, p: 2.1091128843409024e-11\n",
+      "Wyniki bootstrapowej wersji testu T-studenta\n",
+      "\n",
+      "Hipoteza: średnia jest równa 165\n",
+      "Hipoteza alternatywna: średnia jest mniejsza\n",
+      "\n",
+      "p: 0.72\n",
+      "Wartość statystyki testowej z próby: [-229.1025971]\n",
+      "Wartości statystyk z prób boostrapowych:\n",
+      "[-239.4457368], [-201.5], [-176.97470898], [-256.14449047], [-436.1703468], ... (i 95 pozostałych)\n",
+      "\n",
+      "\n",
       "\n"
      ]
     }
@@ -318,8 +371,7 @@
    "source": [
     "#TODO: poprawić kod aby można było podawać kolumny\n",
     "\n",
-    "t_stat, df, cv, p, _ = bootstrap_one_sample(dataset, 165)\n",
-    "pretty_print_full_stats(t_stat, df, cv, p)"
+    "p, t, ts = bootstrap_one_sample(dummy, 165)"
    ]
   },
   {
@@ -343,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 82,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -352,56 +404,15 @@
    },
    "outputs": [],
    "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",
-    "    return (mean(sample_1) - mean(sample_2)) / sed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def df_ind(sample_1, sample_2):\n",
-    "    \"\"\"Funkcja oblicza stopnie swobody dla dwóch próbek niezależnych\"\"\"\n",
-    "    return len(sample_1) + len(sample_2) - 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def bootstrap_independent(sample_1, sample_2):\n",
-    "    return t_test(\n",
+    "def bootstrap_independent(sample_1, sample_2, alternative=Alternatives.LESS):\n",
+    "    p, t, ts = t_test_ind(\n",
     "        sample_1=sample_1,\n",
     "        sample_2=sample_2,\n",
-    "        df_fn=df_ind,\n",
-    "        t_stat_fn=t_stat_ind\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#TODO: Wyciągnąć wysokości kobiet i mężczyzn oraz poprawić kod aby można było podawać kolumny\n",
-    "t_stat, df, cv, p, _ = bootstrap_independent(dataset, dataset)\n",
-    "pretty_print_full_stats(t_stat, df, cv, p)"
+    "        alternative=alternative,\n",
+    "    )\n",
+    "    \n",
+    "    pretty_print_test(p, t, ts, 'średnie są takie same', alternative)\n",
+    "    return p, t, ts"
    ]
   },
   {
@@ -424,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 83,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -433,49 +444,15 @@
    },
    "outputs": [],
    "source": [
-    "def t_stat_dep(sample_1, sample_2, mu=0):\n",
-    "    \"\"\"Funkcja oblicza wartość statystyki testowej dla dwóch próbek zależnych\"\"\"\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",
-    "    return (mean(differences) - mu) / (stdev(differences) / sqrt(sample_size))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def df_dep(sample_1, sample_2):\n",
-    "    \"\"\"Funkcja oblicza stopnie swobody dla dwóch próbek zależnych\"\"\"\n",
-    "    l1, l2 = len(sample_1), len(sample_2)\n",
-    "    if l1 != l2:\n",
-    "        raise Exception(\"Samples aren't of equal length\")\n",
-    "    return l1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def bootstrap_dependent(sample_1, sample_2):\n",
-    "    return t_test(\n",
+    "def bootstrap_dependent(sample_1, sample_2, alternative=Alternatives.LESS):\n",
+    "    p, t, ts = t_test_dep(\n",
     "        sample_1=sample_1,\n",
     "        sample_2=sample_2,\n",
-    "        df_fn=df_dep,\n",
-    "        t_stat_fn=t_stat_dep\n",
-    "    )"
+    "        alternative=alternative,\n",
+    "    )\n",
+    "    \n",
+    "    pretty_print_test(p, t, ts, 'średnie są takie same', alternative)\n",
+    "    return p, t, ts"
    ]
   },
   {
@@ -503,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 84,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -532,76 +509,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Statystyka testowa dla jednej próby:\n",
-      "1.414213562373095 - z naszej funkcji\n",
-      "[1.41421356] - z gotowej biblioteki\n",
-      "\n",
-      "Statystyka testowa dla dwóch prób niezależnych:\n",
-      "-3.0 - z naszej funkcji\n",
-      "[-3.] - z gotowej biblioteki\n",
-      "\n",
-      "Statystyka testowa dla dwóch prób zależnych:\n",
-      "-1.6329931618554525 - z naszej funkcji\n",
-      "[-1.63299316] - z gotowej biblioteki\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Testy dla samych statystyk testowych\n",
-    "def pretty_print_stats(t_stat_selfmade, t_stat_lib, suffix):\n",
-    "    print(f'Statystyka testowa dla {suffix}:')\n",
-    "    print(t_stat_selfmade, '- z naszej funkcji')\n",
-    "    print(t_stat_lib, '- z gotowej biblioteki')\n",
-    "    print()\n",
-    "    \n",
-    "dummy = pd.DataFrame([1, 2, 3, 4, 5])\n",
-    "dummy2 = pd.DataFrame([4, 5, 6, 7, 8])\n",
-    "dummy3 = pd.DataFrame([1, 3 , 3, 4, 6])\n",
-    "\n",
-    "t_stat_selfmade = t_stat_single(dummy, 2)\n",
-    "t_stat_lib, _ = ttest_1samp(dummy, 2)\n",
-    "pretty_print_stats(t_stat_selfmade, t_stat_lib, 'jednej próby')\n",
-    "\n",
-    "t_stat_selfmade = t_stat_ind(dummy, dummy2)\n",
-    "t_stat_lib, _ = ttest_ind(dummy, dummy2)\n",
-    "pretty_print_stats(t_stat_selfmade, t_stat_lib, 'dwóch prób niezależnych')\n",
-    "\n",
-    "t_stat_selfmade = t_stat_dep(dummy, dummy3)\n",
-    "t_stat_lib, _ = ttest_rel(dummy, dummy3)\n",
-    "pretty_print_stats(t_stat_selfmade, t_stat_lib, 'dwóch prób zależnych')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
       "Statystyki dla jednej próby:\n",
-      "t: 1.8073147056683616, df: 5, cv: 2.015048372669157, p: 0.13052275003443325\n",
+      "Wyniki bootstrapowej wersji testu T-studenta\n",
+      "\n",
+      "Hipoteza: średnia jest równa 2\n",
+      "Hipoteza alternatywna: średnia jest mniejsza\n",
+      "\n",
+      "p: 0.35\n",
+      "Wartość statystyki testowej z próby: [1.41421356]\n",
+      "Wartości statystyk z prób boostrapowych:\n",
+      "[2.44948974], [3.13785816], [1.72328087], [0.27216553], [1.17669681], ... (i 95 pozostałych)\n",
       "\n",
-      "Statystyki dla dwóch prób zależnych:\n",
-      "t: 3.0790273716290404, df: 5, cv: 2.015048372669157, p: 0.027500015466573435\n",
       "\n",
-      "Statystyki dla dwóch prób niezależnych:\n",
-      "t: 2.8109511013364576, df: 8, cv: 1.8595480375228421, p: 0.02280961069987497\n",
       "\n"
      ]
     }
@@ -609,22 +535,66 @@
    "source": [
     "# Testy z bootstrappowaniem\n",
     "\n",
-    "def pretty_print_full_stats(t_stat, df, cv, p):\n",
-    "    print(f't: {t_stat}, df: {df}, cv: {cv}, p: {p}\\n')\n",
-    "\n",
-    "print(type(dummy))\n",
-    "\n",
     "print('Statystyki dla jednej próby:')\n",
-    "t_stat, df, cv, p, _ = bootstrap_one_sample(dummy, 2)\n",
-    "pretty_print_full_stats(t_stat, df, cv, p)\n",
-    "\n",
+    "p, t, ts = bootstrap_one_sample(dummy, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Statystyki dla dwóch prób zależnych:\n",
+      "Wyniki bootstrapowej wersji testu T-studenta\n",
+      "\n",
+      "Hipoteza: średnie są takie same\n",
+      "Hipoteza alternatywna: średnia jest mniejsza\n",
+      "\n",
+      "p: 1.0\n",
+      "Wartość statystyki testowej z próby: [10.61445555]\n",
+      "Wartości statystyk z prób boostrapowych:\n",
+      "[-2.66666667], [-0.14359163], [0.21199958], [0.11470787], [0.76696499], ... (i 95 pozostałych)\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
     "print('Statystyki dla dwóch prób zależnych:')\n",
-    "t_stat, df, cv, p, _ = bootstrap_dependent(dummy2, dummy3)\n",
-    "pretty_print_full_stats(t_stat, df, cv, p)\n",
-    "\n",
+    "p, t, ts = bootstrap_dependent(dummy2, dummy3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Statystyki dla dwóch prób niezależnych:\n",
+      "Wyniki bootstrapowej wersji testu T-studenta\n",
+      "\n",
+      "Hipoteza: średnie są takie same\n",
+      "Hipoteza alternatywna: średnia jest mniejsza\n",
+      "\n",
+      "p: 0.95\n",
+      "Wartość statystyki testowej z próby: [2.4140394]\n",
+      "Wartości statystyk z prób boostrapowych:\n",
+      "[-2.20937908], [0.13187609], [-0.81110711], [-0.94280904], [-0.77151675], ... (i 95 pozostałych)\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
     "print('Statystyki dla dwóch prób niezależnych:')\n",
-    "t_stat, df, cv, p, _ = bootstrap_independent(dummy2, dummy3)\n",
-    "pretty_print_full_stats(t_stat, df, cv, p)"
+    "p, t, ts = bootstrap_independent(dummy2, dummy3)"
    ]
   }
  ],
@@ -633,8 +603,12 @@
    "hash": "11938c6bc6919ae2720b4d5011047913343b08a43b18698fd82dedb0d4417594"
   },
   "kernelspec": {
-   "display_name": "Python 3.9.1 64-bit",
-   "language": "python",
+   "display_name": "Python 3.8.10 64-bit",
+   "metadata": {
+    "interpreter": {
+     "hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90"
+    }
+   },
    "name": "python3"
   },
   "language_info": {
@@ -648,8 +622,7 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.1"
-  },
-  "orig_nbformat": 4
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2