Chapter3_GroupComparisons.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c47e232c",
   "metadata": {},
   "source": [
    "## Reading Population Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1e79680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              name team       position  height  weight    age\n",
      "0    Adam_Donachie  BAL        Catcher      74   180.0  22.99\n",
      "1        Paul_Bako  BAL        Catcher      74   215.0  34.69\n",
      "2  Ramon_Hernandez  BAL        Catcher      72   210.0  30.78\n",
      "3     Kevin_Millar  BAL  First_Baseman      72   210.0  35.43\n",
      "4      Chris_Gomez  BAL  First_Baseman      73   188.0  35.71\n",
      "(1034, 6)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "mlb=pd.read_csv('mlb.csv')\n",
    "print(mlb.head())\n",
    "print(mlb.shape) \n",
    "# shows the shape of the data - number of rows and columns\n",
    "# population - 1034 people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e89e991",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            height       weight          age\n",
      "count  1034.000000  1033.000000  1034.000000\n",
      "mean     73.697292   201.689255    28.736712\n",
      "std       2.305818    20.991491     4.320310\n",
      "min      67.000000   150.000000    20.900000\n",
      "25%      72.000000   187.000000    25.440000\n",
      "50%      74.000000   200.000000    27.925000\n",
      "75%      75.000000   215.000000    31.232500\n",
      "max      83.000000   290.000000    48.520000\n"
     ]
    }
   ],
   "source": [
    "# Summary Statistics\n",
    "\n",
    "print(mlb.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "983ebe66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAFMjzAAAAFMjzAAAAFPjCcAATKmwsFBLlixRVlaWwsPDNXbsWLm7uxtdFgADEGYAmM7EiRO1cOFCFRUV2dsSExOVkJCgefPmGVgZACMYepmpqKhIzz33nMLCwuTl5aVWrVpp5syZKikpkSRduHBBkyZNUnR0tBo2bKjAwEANHz5cR44cMbJsAAaaOHGi5s+fL39/f61YsUJHjx7VihUr5O/vr/nz52vixIlGlwiglhn61uzZs2dr4cKFevPNN9WuXTtt2bJFDz30kGbNmqWnnnpKeXl5uvfeezV69Ghdf/31OnnypOLj41VUVKQtW7ZU6hy8NRu4chQWFqphw4by9/fX4cOH5eb22+RyUVGRgoKCdOLECZ05c4ZLToDJmeat2ZmZmRowYIDi4uIkSaGhofrHP/5hDyp+fn5av369w2deeeUV3XzzzcrJyVFISEiZMc+fP6/z58/b961Waw1+AwC1acmSJSoqKtKsWbMcgowkubm5aebMmRozZoyWLFmi+Ph4Y4oEUOsMvczUrVs3bdiwQXv37pUk7dixQ19++aX69etX4Wfy8vJksVjUqFGjco8nJSXJz8/PvgUHB9dE6QAMkJWVJUnq379/ucdL20v7AagfDA0zkyZN0uDBg9W2bVs1aNBAHTt2VHx8vAYPHlxu/3PnzumZZ57RkCFDKpxymjx5svLy8uzboUOHavIrAKhF4eHhkqS0tLRyj5e2l/YDUD8Yumbm3XffVWJioubPn6927drp22+/VXx8vBYsWKARI0Y49L1w4YIGDRqknJwcff7555Ve/8KaGeDKwZoZoP5w5vfb0JmZxMREPfPMM3rggQcUHR2tYcOGKSEhQUlJSQ79Lly4oPvuu08HDhzQ+vXrCSVAPeXu7q6EhAQdO3ZMQUFBWr58uY4cOaLly5crKChIx44dU0JCAkEGqGcMXQBcUFAgFxfHPOXq6mq/NVv6Lcjs27dPGzdulL+/f22XCaAOKX2OzMKFCzVmzBh7u5ubmxITE3nODFAPGRpm/vKXv2j27NkKCQlRu3bttH37di1YsECjRo2SdHHa+N5779W2bduUlpam4uJi5ebmSpKuueYa/usLqKfmzZunWbNm8QRgAJIMXjOTn5+v559/Xh9++KGOHz+uwMBADR48WFOnTpW7u7uys7MVFhZW7mc3btyo2NjYy56DNTMAAJiPM7/fhoaZ2kCYAQDAfEyzABgAAODPIswAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTM/R1BgBQVYWFhbzOAIAkwgwAE5o4caIWLlyooqIie1tiYqISEhJ40SRQD3GZCYCpTJw4UfPnz5e/v79WrFiho0ePasWKFfL399f8+fM1ceJEo0sEUMt4NxMA0ygsLFTDhg3l7++vw4cPy83tt8nloqIiBQUF6cSJEzpz5gyXnACT491MAK5IS5YsUVFRkWbNmuUQZCTJzc1NM2fOVFFRkZYsWWJQhQCMQJgBYBpZWVmSpP79+5d7vLS9tB+A+oEwA8A0wsPDJUlpaWnlHi9tL+0HoH5gzQwA02DNDFB/sGYGwBXJ3d1dCQkJOnbsmIKCgrR8+XIdOXJEy5cvV1BQkI4dO6aEhASCDFDP8JwZAKZS+hyZhQsXasyYMfZ2Nzc3JSYm8pwZoB7iMhMAU+IJwMCVzZnfb8IMAACoc1gzAwAA6g3CDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDAAAMDXCDABTysvLU7du3RQSEqJu3bopLy/P6JIAGIQXTQIwnYiICGVlZdn3Dx06pEaNGik8PFz79+83sDIARjB0ZqaoqEjPPfecwsLC5OXlpVatWmnmzJkqKSmx97HZbJo+fboCAwPl5eWl2NhY7dq1y8CqARjp90GmT58+yszMVJ8+fSRJWVlZioiIMLI8AAYwdGbmhRde0LJly/Tmm2+qXbt22rJlix566CH5+fnpqaeekiTNmzdPCxYs0KpVq9SmTRvNmjVLPXv21I8//igfHx8jywdQy/Ly8uxB5syZM/L29pYkffrppyooKFDDhg2VlZWlvLw8+fn5GVkqgFpk6MxMZmamBgwYoLi4OIWGhuree+9Vr169tGXLFkkXZ2UWLVqkZ599Vvfcc4/at2+vN998UwUFBXrnnXfKHfP8+fOyWq0OG4ArQ1xcnKSLMzKlQaaUt7e3evXq5dAPQP1gaJjp1q2bNmzYoL1790qSduzYoS+//FL9+vWTJB04cEC5ubn2/4GSJA8PD8XExOirr74qd8ykpCT5+fnZt+Dg4Jr/IgBqRU5OjiRp2rRp5R5/7rnnHPoBqB8MDTOTJk3S4MGD1bZtWzVo0EAdO3ZUfHy8Bg8eLEnKzc2VJDVr1szhc82aNbMf+6PJkycrLy/Pvh06dKhmvwSAWhMSEiJJmjFjRrnHZ82a5dAPQP1g6JqZ9957TykpKXrnnXfUrl07ffvtt4qPj1dgYKBGjBhh72exWBw+Z7PZyrSV8vDwkIeHR43WDcAYH3/8sRo1aqS1a9eqoKDA4VJTQUGB1q1bZ+8HoP4wdGYmMTFRzzzzjB544AFFR0dr2LBhSkhIUFJSkiQpICBAksrMwhw/frzMbA2AK5+fn5/Cw8MlSQ0bNlTv3r21adMm9e7dWw0bNpQkhYeHs/gXqGcMDTMFBQVycXEswdXV1X5rdlhYmAICArR+/Xr78cLCQmVkZKhLly61WiuAumH//v32QLNu3Tp1797dPiPDc2aA+snQy0x/+ctfNHv2bIWEhKhdu3bavn27FixYoFGjRkm6eHkpPj5ec+bMUevWrdW6dWvNmTNH3t7eGjJkiJGlAzDQ/v37lZeXp7i4OOXk5CgkJEQff/wxMzJAPWWx2Ww2o06en5+v559/Xh9++KGOHz+uwMBADR48WFOnTpW7u7uki+tjZsyYoddee00nT57ULbfcosWLF6t9+/aVOofVapWfn5/y8vLk6+tbk18HAABUE2d+vw0NM7WBMAMAgPk48/vNiyYBAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpEWYAAICpuRldAID6paCgQHv27KmWsc6ePavs7GyFhobKy8urWsZs27atvL29q2UsALWDMAOgVu3Zs0c33HCD0WVUaOvWrerUqZPRZQBwAmEGQK1q27attm7dWi1j7d69W0OHDlVKSooiIyOrZcy2bdtWyzgAag9hBkCt8vb2rvaZj8jISGZTgHqMBcAAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUDA0zoaGhslgsZbbHH39cknT69Gk98cQTCgoKkpeXlyIjI7V06VIjSwYAAHWMm5En37x5s4qLi+37O3fuVM+ePTVo0CBJUkJCgjZu3KiUlBSFhoZq3bp1Gjt2rAIDAzVgwACjygYAAHWIoTMzTZo0UUBAgH1LS0tTeHi4YmJiJEmZmZkaMWKEYmNjFRoaqkcffVTXX3+9tmzZUuGY58+fl9VqddgAAMCVq86smSksLFRKSopGjRoli8UiSerWrZvWrFmjn376STabTRs3btTevXvVu3fvCsdJSkqSn5+ffQsODq6trwAAAAxQZ8JMamqqTp06pZEjR9rbXn75ZUVFRSkoKEju7u7q06ePlixZom7dulU4zuTJk5WXl2ffDh06VAvVAwAAoxi6Zub3Vq5cqb59+yowMNDe9vLLL+vrr7/WmjVr1LJlS33xxRcaO3asmjdvrjvvvLPccTw8POTh4VFbZQMAAIPViTBz8OBBpaena/Xq1fa2s2fPasqUKfrwww8VFxcnSbruuuv07bff6sUXX6wwzAAAgPqlTlxmSk5OVtOmTe2hRZIuXLigCxcuyMXFsURXV1eVlJTUdokAAKCOMnxmpqSkRMnJyRoxYoTc3H4rx9fXVzExMUpMTJSXl5datmypjIwMvfXWW1qwYIGBFQMAgLrE8DCTnp6unJwcjRo1qsyxd999V5MnT9aDDz6oX3/9VS1bttTs2bP12GOPGVApAACoiwwPM7169ZLNZiv3WEBAgJKTk2u5IgAAYCZ1Ys0MAABAVRFmAACAqRFmAACAqRFmAACAqRFmAACAqTl1N5PNZlNGRoY2bdqk7OxsFRQUqEmTJurYsaPuvPNOXuoIAABqXaVmZs6ePas5c+YoODhYffv21ccff6xTp07J1dVV+/fv17Rp0xQWFqZ+/frp66+/rumaAQAA7Co1M9OmTRvdcsstWrZsmXr37q0GDRqU6XPw4EG98847uv/++/Xcc89p9OjR1V4sAADAH1UqzHz66adq3779Jfu0bNlSkydP1oQJE3Tw4MFqKQ4AAOByKnWZ6XJB5vfc3d3VunXrKhcEAADgDKfvZlq7dq2+/PJL+/7ixYvVoUMHDRkyRCdPnqzW4gAAAC7H6TCTmJgoq9UqSfr+++81YcIE9evXT//73/80fvz4ai8QAADgUpx+0eSBAwcUFRUlSfrggw/Uv39/zZkzR9u2bVO/fv2qvUAAAIBLcXpmxt3dXQUFBZKk9PR09erVS5J0zTXX2GdsAAAAaovTMzPdunXT+PHj1bVrV33zzTd67733JEl79+5VUFBQtRcIAABwKU7PzLz66qtyc3PT+++/r6VLl6pFixaSLt6+3adPn2ovEAAA4FKcnpkJCQlRWlpamfaFCxdWS0EAAADOqNKLJrOysvTcc89p8ODBOn78uKSLt2zv2rWrWosDAAC4HKfDTEZGhqKjo/Xf//5Xq1ev1unTpyVJ3333naZNm1btBQIAAFyK02HmmWee0axZs7R+/Xq5u7vb23v06KHMzMxqLQ4AAOBynA4z33//ve6+++4y7U2aNNGJEyeqpSgAAIDKcjrMNGrUSEePHi3Tvn37dvudTQAAALXF6TAzZMgQTZo0Sbm5ubJYLCopKdF//vMfPf300xo+fHhN1AgAAFAhp8PM7NmzFRISohYtWuj06dOKiopS9+7d1aVLFz333HM1USMAAECFnH7OTIMGDfT2229r5syZ2r59u0pKStSxY0e1bt26JuoDAAC4JKfDTKnw8HCFh4dXZy0AAABOczrMFBcXa9WqVdqwYYOOHz+ukpISh+OfffZZtRUHAABwOU6HmaeeekqrVq1SXFyc2rdvL4vFUhN1AQAAVIrTYebdd9/VP//5T/Xr168m6gEAAHCK03czubu7KyIioiZqAQAAcJrTYWbChAl66aWXZLPZaqIeAAAAp1TqMtM999zjsP/ZZ5/p008/Vbt27dSgQQOHY6tXr670yUNDQ3Xw4MEy7WPHjtXixYslSbt379akSZOUkZGhkpIStWvXTv/85z8VEhJS6fMAAIArV6XCjJ+fn8N+ee9mqorNmzeruLjYvr9z50717NlTgwYNkiRlZWWpW7duevjhhzVjxgz5+flp9+7d8vT0rJbzAwAA86tUmElOTq6Rkzdp0sRhf+7cuQoPD1dMTIwk6dlnn1W/fv00b948e59WrVpdcszz58/r/Pnz9n2r1VqNFQMAgLrG6TUzBw4c0L59+8q079u3T9nZ2VUupLCwUCkpKRo1apT9nU8ff/yx2rRpo969e6tp06a65ZZblJqaeslxkpKS5OfnZ9+Cg4OrXBMAAKj7nA4zI0eO1FdffVWm/b///a9GjhxZ5UJSU1N16tQp+xjHjx/X6dOnNXfuXPXp00fr1q3T3XffrXvuuUcZGRkVjjN58mTl5eXZt0OHDlW5JgAAUPc5/ZyZ7du3q2vXrmXab731Vj3xxBNVLmTlypXq27evAgMDJcn+ZOEBAwYoISFBktShQwd99dVXWrZsmf1S1B95eHjIw8OjynUAAABzcXpmxmKxKD8/v0x7Xl6ew2JeZxw8eFDp6el65JFH7G2NGzeWm5uboqKiHPpGRkYqJyenSucBAABXHqfDzG233aakpCSH4FJcXKykpCR169atSkUkJyeradOmiouLs7e5u7vrpptu0o8//ujQd+/evWrZsmWVzgMAAK48Tl9mmjdvnrp3765rr71Wt912myRp06ZNslqtVXrJZElJiZKTkzVixAi5uTmWk5iYqPvvv1/du3dXjx49tHbtWn300Uf6/PPPnT4PAAC4Mjk9MxMVFaXvvvtO9913n44fP678/HwNHz5ce/bsUfv27Z0uID09XTk5ORo1alSZY3fffbeWLVumefPmKTo6Wq+//ro++OCDKs8AAQCAK4/FdoW/l8BqtcrPz095eXny9fU1uhwA1Wjbtm264YYbtHXrVnXq1MnocgBUI2d+v52+zCRJp06d0jfffKPjx4/b7zoqNXz48KoMCQAAUCVOh5mPPvpIDz74oM6cOSMfHx9ZLBb7MYvFQpgBAAC1qkpvzR41apTy8/N16tQpnTx50r79+uuvNVEjAABAhZwOMz/99JPGjRsnb2/vmqgHAADAKU6Hmd69e2vLli01UQsAAIDTnF4zExcXp8TERP3www+Kjo5WgwYNHI7fdddd1VYcAADA5TgdZkaPHi1JmjlzZpljFoulyq80AAAAqAqnw8wfb8UGAAAwktNrZgAAAOqSSs/MvPzyy5XqN27cuCoXAwAA4KxKh5mFCxdeto/FYiHMAACAWlXpMHPgwIGarAMAAKBKqvRuJgD10759+5Sfn290GXa7d+92+L91hY+Pj1q3bm10GUC9Uakw8+677+qBBx6o1ICHDh1STk6Ounbt+qcKA1C37Nu3T23atDG6jHINHTrU6BLK2Lt3L4EGqCWVCjNLly7V9OnT9dBDD+muu+5SZGSkw/G8vDz95z//UUpKitLT07Vy5coaKRaAcUpnZFJSUsr8b4BRzp49q+zsbIWGhsrLy8vociRdnCUaOnRonZrBAq50lQozGRkZSktL0yuvvKIpU6aoYcOGatasmTw9PXXy5Enl5uaqSZMmeuihh7Rz5041bdq0pusGYJDIyEh16tTJ6DLsmAUGUOk1M/3791f//v114sQJffnll8rOztbZs2fVuHFjdezYUR07dpSLC4+tAQAAtcvpBcD+/v4aMGBATdQCAADgNKZSAACAqRFmAACAqRFmAACAqRFmAACAqTkdZmbOnKmCgoIy7WfPntXMmTOrpSgAAIDKcjrMzJgxQ6dPny7TXlBQoBkzZlRLUQAAAJXldJix2WyyWCxl2nfs2KFrrrmmWooCAACorEo/Z+bqq6+WxWKRxWJRmzZtHAJNcXGxTp8+rccee6xGigQAAKhIpcPMokWLZLPZNGrUKM2YMUN+fn72Y+7u7goNDVXnzp1rpEgAAICKVDrMjBgxQpIUFhamLl26qEGDBjVWFAAAQGU5/TqDmJgYlZSUaO/evTp+/LhKSkocjnfv3r3aigMAALgcp8PM119/rSFDhujgwYOy2WwOxywWi4qLi6utOAAAgMtxOsw89thjuvHGG/Xxxx+refPm5d7ZBAAAUFucvjV73759mjNnjiIjI9WoUSP5+fk5bM4IDQ213yH1++3xxx8v03fMmDGyWCxatGiRsyUDAIArmNNh5pZbbtH+/fur5eSbN2/W0aNH7dv69eslSYMGDXLol5qaqv/+978KDAyslvMCAIArR6UuM3333Xf2fz/55JOaMGGCcnNzFR0dXeaupuuuu67SJ2/SpInD/ty5cxUeHq6YmBh7208//aQnnnhC//d//6e4uLjLjnn+/HmdP3/evm+1WitdD4CKWYrOqWOAi7xO7ZWO8Fq3inid2quOAS6yFJ0zuhSg3qhUmOnQoYMsFovDgt9Ro0bZ/1167M8sAC4sLFRKSorGjx9vX4dTUlKiYcOGKTExUe3atavUOElJSbxWAagBnqdztG3MVdIXY6QvjK6m7oqUtG3MVdp9OkdSF6PLAeqFSoWZAwcO1HQdSk1N1alTpzRy5Eh72wsvvCA3NzeNGzeu0uNMnjxZ48ePt+9brVYFBwdXZ6lAvXTuqhB1eu203n77bUW2bWt0OXXW7j179OCDD2plvxCjSwHqjUqFmZYtW9Z0HVq5cqX69u1rXxezdetWvfTSS9q2bZtTd0x5eHjIw8OjpsoE6i2bm6e255bobKM2UmAHo8ups87mlmh7bolsbp5GlwLUG07fmr1mzZpy2y0Wizw9PRUREaGwsDCnxjx48KDS09O1evVqe9umTZt0/PhxhYT89l83xcXFmjBhghYtWqTs7GxnSwcAAFcgp8PMwIEDy6yfkRzXzXTr1k2pqam6+uqrKzVmcnKymjZt6rDAd9iwYbrzzjsd+vXu3VvDhg3TQw895GzZAADgCuX0LQnr16/XTTfdpPXr1ysvL095eXlav369br75ZqWlpemLL77QiRMn9PTTT1dqvJKSEiUnJ2vEiBFyc/stW/n7+6t9+/YOW4MGDRQQEKBrr73W2bIBAMAVyumZmaeeekrLly9Xly6/rdK/44475OnpqUcffVS7du3SokWLHO52upT09HTl5ORUuj8AAMDvOR1msrKy5OvrW6bd19dX//vf/yRJrVu31i+//FKp8Xr16lXmklVFWCcDAAD+yOnLTDfccIMSExP1888/29t+/vlnTZw4UTfddJOki688CAoKqr4qAQAAKuD0zMzKlSs1YMAABQUFKTg4WBaLRTk5OWrVqpX+/e9/S5JOnz6t559/vtqLBQAA+COnw8y1116r3bt36//+7/+0d+9e2Ww2tW3bVj179pSLy8WJnoEDB1Z3nQAAAOVyOsxIF2/D7tOnj/r06VPd9QAAADilUmHm5Zdf1qOPPipPT0+9/PLLl+zrzKsHAAAA/qxKhZmFCxfqwQcflKenpxYuXFhhP4vFQpgBAAC1yukXTdbGSycBAAAqy+lbs0sVFhbqxx9/VFFRUXXWAwAA4BSnw0xBQYEefvhheXt7q127dsrJyZF0ca3M3Llzq71AAACAS3E6zEyePFk7duzQ559/Lk/P315xf+edd+q9996r1uIAAAAux+lbs1NTU/Xee+/p1ltvlcVisbdHRUUpKyurWosDAAC4HKdnZn7++Wc1bdq0TPuZM2ccwg0AAEBtcDrM3HTTTfr444/t+6UBZsWKFercuXP1VQYAAFAJTl9mSkpKUp8+ffTDDz+oqKhIL730knbt2qXMzExlZGTURI0AAAAVcnpmpkuXLvrPf/6jgoIChYeHa926dWrWrJkyMzN1ww031ESNAAAAFarSu5mio6P15ptvVnctAAAATqt0mLFarZXq5+vrW+ViAAAAnFXpMNOoUaNL3q1ks9lksVhUXFxcLYUBAABURqXDzMaNG+3/ttls6tevn15//XW1aNGiRgoDAACojEqHmZiYGId9V1dX3XrrrWrVqlW1FwUAAFBZVX7RJAAAQF1AmAEAAKb2p8IMry8AAABGq/SamXvuucdh/9y5c3rsscfUsGFDh/bVq1dXT2UAAACVUOkw4+fn57A/dOjQai8GAADAWZUOM8nJyTVZBwAAQJWwABgAAJgaYQYAAJgaYQYAAJgaYQYAAJgaYQYAAJiaoWEmNDRUFoulzPb444/rwoULmjRpkqKjo9WwYUMFBgZq+PDhOnLkiJElAwCAOsbQMLN582YdPXrUvq1fv16SNGjQIBUUFGjbtm16/vnntW3bNq1evVp79+7VXXfdZWTJAACgjqn0c2ZqQpMmTRz2586dq/DwcMXExMhisdjDTalXXnlFN998s3JychQSElLumOfPn9f58+ft+1artfoLB+qhgoICSdK2bdsMruQ3Z8+eVXZ2tkJDQ+Xl5WV0OZKk3bt3G10CUO8YGmZ+r7CwUCkpKRo/fnyF73zKy8uTxWJRo0aNKhwnKSlJM2bMqKEqgfprz549kqTRo0cbXIk5+Pj4GF0CUG9YbDabzegiJOmf//ynhgwZopycHAUGBpY5fu7cOXXr1k1t27ZVSkpKheOUNzMTHBysvLw8+fr61kjtQH3wyy+/KDU1VW3btpW3t7fR5Ui6OAsydOhQpaSkKDIy0uhy7Hx8fNS6dWujywBMzWq1ys/Pr1K/33VmZmblypXq27dvuUHmwoULeuCBB1RSUqIlS5ZcchwPDw95eHjUVJlAvdW4cWM98sgjRpdRrsjISHXq1MnoMgAYpE6EmYMHDyo9Pb3cN25fuHBB9913nw4cOKDPPvuM2RUAAOCgToSZ5ORkNW3aVHFxcQ7tpUFm37592rhxo/z9/Q2qEAAA1FWGh5mSkhIlJydrxIgRcnP7rZyioiLde++92rZtm9LS0lRcXKzc3FxJ0jXXXCN3d3ejSgYAAHWI4WEmPT1dOTk5GjVqlEP74cOHtWbNGklShw4dHI5t3LhRsbGxtVQhAACoywwPM7169VJ5N1SFhoaW2w4AAPB7vJsJAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYmqFhJjQ0VBaLpcz2+OOPS5JsNpumT5+uwMBAeXl5KTY2Vrt27TKyZAAAUMcYGmY2b96so0eP2rf169dLkgYNGiRJmjdvnhYsWKBXX31VmzdvVkBAgHr27Kn8/HwjywYAAHWIoWGmSZMmCggIsG9paWkKDw9XTEyMbDabFi1apGeffVb33HOP2rdvrzfffFMFBQV65513Khzz/PnzslqtDhsAALhy1Zk1M4WFhUpJSdGoUaNksVh04MAB5ebmqlevXvY+Hh4eiomJ0VdffVXhOElJSfLz87NvwcHBtVE+AAAwSJ0JM6mpqTp16pRGjhwpScrNzZUkNWvWzKFfs2bN7MfKM3nyZOXl5dm3Q4cO1VjNAADAeG5GF1Bq5cqV6tu3rwIDAx3aLRaLw77NZivT9nseHh7y8PCokRoBAEDdUydmZg4ePKj09HQ98sgj9raAgABJKjMLc/z48TKzNQAAoP6qE2EmOTlZTZs2VVxcnL0tLCxMAQEB9jucpIvrajIyMtSlSxcjygQAAHWQ4ZeZSkpKlJycrBEjRsjN7bdyLBaL4uPjNWfOHLVu3VqtW7fWnDlz5O3trSFDhhhYMQAAqEsMDzPp6enKycnRqFGjyhybOHGizp49q7Fjx+rkyZO65ZZbtG7dOvn4+BhQKQAAqIssNpvNZnQRNclqtcrPz095eXny9fU1uhwA1Wjbtm264YYbtHXrVnXq1MnocgBUI2d+v+vEmhkAAICqIswAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTMzzM/PTTTxo6dKj8/f3l7e2tDh06aOvWrfbjp0+f1hNPPKGgoCB5eXkpMjJSS5cuNbBiAABQl7gZefKTJ0+qa9eu6tGjhz799FM1bdpUWVlZatSokb1PQkKCNm7cqJSUFIWGhmrdunUaO3asAgMDNWDAAOOKBwAAdYKhYeaFF15QcHCwkpOT7W2hoaEOfTIzMzVixAjFxsZKkh599FG99tpr2rJlS7lh5vz58zp//rx932q11kjtAKqmoKBAe/bsqZaxdu/e7fB/q0Pbtm3l7e1dbeMBqHkWm81mM+rkUVFR6t27tw4fPqyMjAy1aNFCY8eO1ejRo+19HnvsMW3dulWpqakKDAzU559/rrvuukuffvqpunXrVmbM6dOna8aMGWXa8/Ly5OvrW6PfB8Dlbdu2TTfccIPRZVRo69at6tSpk9FlAPWe1WqVn59fpX6/DQ0znp6ekqTx48dr0KBB+uabbxQfH6/XXntNw4cPlyQVFhZq9OjReuutt+Tm5iYXFxe9/vrrGjZsWLljljczExwcTJgB6ojqnJk5e/assrOzFRoaKi8vr2oZk5kZoG5wJswYepmppKREN954o+bMmSNJ6tixo3bt2qWlS5faw8zLL7+sr7/+WmvWrFHLli31xRdfaOzYsWrevLnuvPPOMmN6eHjIw8OjVr8HgMrz9vau1pmPrl27VttYAMzJ0DDTvHlzRUVFObRFRkbqgw8+kHTxv7qmTJmiDz/8UHFxcZKk6667Tt9++61efPHFcsMMAACoXwy9Nbtr16768ccfHdr27t2rli1bSpIuXLigCxcuyMXFsUxXV1eVlJTUWp0AAKDuMnRmJiEhQV26dNGcOXN033336ZtvvtHy5cu1fPlySZKvr69iYmKUmJgoLy8vtWzZUhkZGXrrrbe0YMECI0sHAAB1hKELgCUpLS1NkydP1r59+xQWFqbx48c73M2Um5uryZMna926dfr111/VsmVLPfroo0pISJDFYrns+M4sIAIAAHWDae5mqg2EGQAAzMeZ32/DX2cAAADwZxBmAACAqRFmAACAqRFmAACAqRFmAACAqRFmAACAqRFmAACAqRFmAACAqRn6OgMAqKrCwkItWbJEWVlZCg8P19ixY+Xu7m50WQAMQJgBYDoTJ07UwoULVVRUZG9LTExUQkKC5s2bZ2BlAIzAZSYApjJx4kTNnz9f/v7+WrFihY4ePaoVK1bI399f8+fP18SJE40uEUAt491MAEyjsLBQDRs2lL+/vw4fPiw3t98ml4uKihQUFKQTJ07ozJkzXHICTI53MwG4Ii1ZskRFRUWaNWuWQ5CRJDc3N82cOVNFRUVasmSJQRUCMAJhBoBpZGVlSZL69+9f7vHS9tJ+AOoHwgwA0wgPD5ckpaWllXu8tL20H4D6gTUzAEyDNTNA/cGaGQBXJHd3dyUkJOjYsWMKCgrS8uXLdeTIES1fvlxBQUE6duyYEhISCDJAPcNzZgCYSulzZBYuXKgxY8bY293c3JSYmMhzZoB6iMtMAEyJJwADVzZnfr8JMwAAoM5hzQwAAKg3CDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUrvh3M5U+4NhqtRpcCQAAqKzS3+3KvKjgig8z+fn5kqTg4GCDKwEAAM7Kz8+Xn5/fJftc8e9mKikp0ZEjR+Tj4yOLxWJ0OQCqkdVqVXBwsA4dOsS714ArjM1mU35+vgIDA+XiculVMVd8mAFw5eJFsgAkFgADAACTI8wAAABTI8wAMC0PDw9NmzZNHh4eRpcCwECsmQEAAKbGzAwAADA1wgwAADA1wgwAADA1wgwAADA1wgyAKomNjVV8fLx9PzQ0VIsWLTKsnsqaPn26OnToUGfGAfDnEWaAemrkyJGyWCxltv3799fI+aZPn24/h6urq4KDg/XII4/o559/rpHzVSeLxaLU1FSHtqefflobNmwwpiAADq74F00CqFifPn2UnJzs0NakSZMaO1+7du2Unp6u4uJibd++XQ8//LB++uknffrppzV2zppy1VVX6aqrrjK6DABiZgao1zw8PBQQEOCwubq6auTIkRo4cKBD3/j4eMXGxv6p87m5uSkgIEAtWrRQ//79NW7cOK1bt05nz55VSUmJZs6cqaCgIHl4eKhDhw5au3at/bPZ2dmyWCx699131aVLF3l6eqpdu3b6/PPP7X1WrVqlRo0aOZwzNTX1ki+Z3bx5s3r27KnGjRvLz89PMTEx2rZtm/14aGioJOnuu++WxWKx7//xMlNl61+9erV69Oghb29vXX/99crMzHT+DwnAAWEGgGG8vLxUUlKioqIivfTSS/rb3/6mF198Ud9995169+6tu+66S/v27XP4TGJioiZMmKDt27erS5cuuuuuu3TixIkq15Cfn68RI0Zo06ZN+vrrr9W6dWv169dP+fn5ki6GHUlKTk7W0aNH7ft/VNn6n332WT399NP69ttv1aZNGw0ePFhFRUVVrh8AYQao19LS0uyXS6666ioNGjSo1s69Z88eLV26VDfffLN8fHz04osvatKkSXrggQd07bXX6oUXXlCHDh3KLCp+4okn9P/+3/9TZGSkli5dKj8/P61cubLKddx+++0aOnSoIiMjFRkZqddee00FBQXKyMiQ9Ntlt0aNGikgIKDCy3CVrf/pp59WXFyc2rRpoxkzZujgwYM1tk4JqC9YMwPUYz169NDSpUvt+w0bNqzR833//fe66qqrVFxcrPPnzys2NlbLly+X1WrVkSNH1LVrV4f+Xbt21Y4dOxzaOnfubP+3m5ubbrzxRu3evbvKNR0/flxTp07VZ599pmPHjqm4uFgFBQXKycmp9BjO1H/dddfZ/928eXN7DW3btq3ydwDqO8IMUI81bNhQERERZdpdXFz0x9e2Xbhw4U+f79prr9WaNWvk6uqqwMBA+wsirVarJJVZ22Kz2S653qVUaZ+q1D1y5Ej9/PPPWrRokVq2bCkPDw917txZhYWFlf5ef6yjVHn1N2jQoEz/kpISp88F4DdcZgJQRpMmTXT06FGHtm+//fZPj+vu7q6IiAiFhYU5vOna19dXgYGB+vLLLx36f/XVV4qMjHRo+/rrr+3/Lioq0tatW+2zGk2aNFF+fr7OnDlT6bo3bdqkcePGqV+/fmrXrp08PDz0yy+/OPRp0KCBiouLKxzDmfoBVD9mZgCUcfvtt2v+/Pl666231LlzZ6WkpGjnzp3q2LFjjZ0zMTFR06ZNU3h4uDp06KDk5GR9++23evvttx36LV68WK1bt1ZkZKQWLlyokydPatSoUZKkW265Rd7e3poyZYqefPJJffPNN1q1atUlzxsREaG///3vuvHGG2W1WpWYmCgvLy+HPqGhodqwYYO6du0qDw8PXX311VWuH0D1Y2YGQBm9e/fW888/r4kTJ+qmm25Sfn6+hg8fXqPnHDdunCZMmKAJEyYoOjpaa9eu1Zo1a9S6dWuHfnPnztULL7yg66+/Xps2bdK///1vNW7cWJJ0zTXXKCUlRZ988omio6P1j3/8Q9OnT7/ked944w2dPHlSHTt21LBhwzRu3Dg1bdrUoc/f/vY3rV+/XsHBwRUGusrWD6D6WWx/vMAMAHVQdna2wsLCtH37dl4jAMABMzMAAMDUCDMAAMDUuMwEAABMjZkZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgaoQZAABgav8fAn2Z93tPDWkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.boxplot([mlb['height']])\n",
    "ax1.set_ylabel('Height (Inches)')\n",
    "plt.title('MLB Player Heights')\n",
    "plt.xticks([1], ['Full Population'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "99d0633f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Samples\n",
    "\n",
    "# “Recruiting a sample is something you should do carefully, to make sure the sample resembles the full population as much as possible”\n",
    "# best way to collect sample = random sample\n",
    "\n",
    "sample1=mlb.sample(n=30,random_state=8675309)\n",
    "sample2=mlb.sample(n=30,random_state=1729)\n",
    "# sample() randomly selects samples\n",
    "\n",
    "sample3=[71, 72, 73, 74, 74, 76, 75, 75, 75, 76, 75, 77, 76, 75, 77, 76, 75,76, 76, 75, 75, 81,77, 75, 77, 75,\\\n",
    "         77, 77, 75, 75]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36b18c80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.boxplot([mlb['height'],sample1['height'],sample2['height'],np.array(sample3)])\n",
    "ax1.set_ylabel('Height (Inches)')\n",
    "plt.title('MLB Player Heights')\n",
    "plt.xticks([1,2,3,4], ['Full Population','Sample 1','Sample 2','Sample 3'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d53fd8c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73.8\n",
      "74.36666666666666\n",
      "75.43333333333334\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(sample1['height']))\n",
    "print(np.mean(sample2['height']))\n",
    "print(np.mean(sample3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65cc5c3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73.6972920696325\n"
     ]
    }
   ],
   "source": [
    "# expected value - mean height of full population\n",
    "print(np.mean(mlb['height']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "026c67ca",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8333333333333286, -0.30000000000001137, -0.10000000000000853, -0.1666666666666572, 0.06666666666667709, -0.9666666666666686, 0.7999999999999972, 0.9333333333333371, -0.5333333333333314, -0.20000000000000284]\n"
     ]
    }
   ],
   "source": [
    "# Differences Between Sample Data\n",
    "\n",
    "alldifferences=[]\n",
    "for i in range(1000):\n",
    "    newsample1=mlb.sample(n=30,random_state=i*2)\n",
    "    newsample2=mlb.sample(n=30,random_state=i*2+1)\n",
    "    alldifferences.append(newsample1['height'].mean()-newsample2['height'].mean())\n",
    "\n",
    "print(alldifferences[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c091854a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ShreenidhiAyinala/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set()\n",
    "ax=sns.distplot(alldifferences)\n",
    "plt.title(\"Differences Between Sample Means\")\n",
    "plt.xlabel('Difference Between Means (Inches)')\n",
    "plt.ylabel('Relative Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e33862c",
   "metadata": {},
   "source": [
    "“A histogram showing the distribution of differences between mean heights of random samples, creating an approximate bell curve pattern”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "32bde760",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bell curve\n",
    "# “One powerful theoretical result in statistics is called the central limit theorem, and it states that under a \n",
    "# certain set of common conditions, differences between means of samples will be distributed in a shape that’s approximately a bell curve.”\n",
    "# “The technical conditions that make this theorem true are that the random samples are independent and identically \n",
    "# distributed (that is, random draws from the same population) and that the population has a finite expected value and a finite variance.”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9c07b16f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "largedifferences=[diff for diff in alldifferences if abs(diff)>=1.6]\n",
    "print(len(largedifferences))\n",
    "# mlb population have means that differ by 1.6 or more only about 8 in 1000 times or 0.8 percent of the time\n",
    "# ^ p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e279fb9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# “low p-value causes us to reject the notion that these two groups come from the same population”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ab7558a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "314\n"
     ]
    }
   ],
   "source": [
    "smalldifferences=[diff for diff in alldifferences if abs(diff)>=0.6]\n",
    "print(len(smalldifferences))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f815e1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# “The p-values we’ve calculated here have led us to accept the notion that sample1 and sample2 come from the same \n",
    "# population, and to reject the notion that sample1 and sample3 come from the same population.”"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "816ee421",
   "metadata": {},
   "source": [
    "## Performing Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9080fe7",
   "metadata": {},
   "source": [
    "Hypothesis 0 sample1 and sample3 are random samples from the same population.\n",
    "\n",
    "Hypothesis 1 sample1 and sample3 are not random samples from the same population.\n",
    "\n",
    "Hypothesis 0 - null hypothesis\n",
    "\n",
    "“The null hypothesis asserts that both samples are randomly drawn from one population (our baseball player dataset), with just one mean and one standard deviation.”\n",
    "\n",
    "Hypothesis 1 - alternative hypothesis\n",
    "\n",
    "“The alternative hypothesis asserts that the samples are randomly drawn from two totally different populations, each with its own mean, its own standard deviation, and all of its own unique characteristics.”\n",
    "\n",
    "\n",
    "“The p-value size that we believe justifies rejecting Hypothesis 0 is called the significance level”\n",
    "- most common significance level is 5 percent\n",
    "\n",
    "“When we find a p-value that’s less than our chosen significance level, we say that the difference between our groups is statistically significant”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2098af06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-1.0839563860213952, pvalue=0.2828695892305152)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The t-Test\n",
    "\n",
    "import scipy.stats\n",
    "scipy.stats.ttest_ind(sample1['height'],sample2['height'])\n",
    "# ttest_ind checks for the difference between the samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "54e22b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0.283 is higher than 0.05 -> indicates samples are from same population\n",
    "# -> we do not reject the null hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fc923484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-3.108694797644094, pvalue=0.002910554741439718)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.ttest_ind(sample1['height'],sample3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "129c9625",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0.003 < 0.05, suggests rejecting null hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98f8a92c",
   "metadata": {},
   "source": [
    "ttest_ind -> independent variables - no relationship between individuals in each of the samples\n",
    "\n",
    "ttest_rel -> when observations from different samples are related\n",
    "\n",
    "ttest_equal_var=False -> Welch's test -> compares samples when we don't want to assume that the samples have equal variance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39ca34a3",
   "metadata": {},
   "source": [
    "t-test is a parametric test -> relies on assumptions about the distribution of the data in our population\n",
    "\n",
    "“The t-test relies on several technical assumptions: \n",
    "- first, that the groups being compared should have sample means that follow a bell curve; \n",
    "- second, that the variances of the groups being compared should be identical (unless using Welch’s t-test); \n",
    "- third, that the two groups are independent of each other. \n",
    "\n",
    "If these assumptions are not met, the t-test is not completely accurate, though it’s rarely too far from the truth even if the assumptions are not met.”"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb5ebbcf",
   "metadata": {},
   "source": [
    "nonparametric statistics -> fewer assumptions about the distribution of the data\n",
    "\n",
    "=> Mann-Whitney U test (or Wilcoxon rank-sum test) -> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c6cc0524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=392.0, pvalue=0.38913738408151577)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.mannwhitneyu(sample1['height'],sample2['height'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6fa1d0e",
   "metadata": {},
   "source": [
    "#### Nuances of Hypothesis testing\n",
    "\n",
    "important note about p-value: “it tells you the likelihood of observing data, after assuming a null hypothesis to be true”\n",
    "\n",
    "“Another important nuance is the difference between accepting a hypothesis and failing to reject it. \n",
    "- Hypothesis testing has only two possible outcomes: either we reject a null hypothesis, or we decide not to reject the null hypothesis. \n",
    "- Failing to reject something is not quite the same as wholeheartedly accepting it, and just because a p-value is not below a significance threshold does not mean that two groups are certainly the same. \n",
    "\n",
    "Just because one t-test fails to lead to a rejection of the null hypothesis does not mean that the null hypothesis is certainly true.”\n",
    "\n",
    "Anna Karenina principle: “all happy families are alike; each unhappy family is unhappy in its own way”\n",
    "\n",
    "=> “Statistics has an analogous principle: all acceptances of the null hypothesis are alike, but each rejection of the null hypothesis happens for a different reason”\n",
    "\n",
    "\n",
    "\n",
    "##### Difference between statistical significance and practical significance\n",
    "- “we might wish to ignore the results of a t-test, since even though a statistically detectable difference exists, it’s not a difference that has any practical consequence. \n",
    "- Practical significance is always an important thing to consider during the process of hypothesis testing.”"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f78769c",
   "metadata": {},
   "source": [
    "## Comparing Groups in a Practical Context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "370e8623",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   userid  spending  age  visits\n",
      "0       1      1250   31     126\n",
      "1       2       900   27       5\n",
      "2       3         0   30     459\n",
      "3       4      2890   22      18\n",
      "4       5      1460   38      20\n",
      "   userid  spending  age  visits\n",
      "0      31      1499   32      12\n",
      "1      32       799   23      40\n",
      "2      33      1200   45      22\n",
      "3      34         0   59     126\n",
      "4      35      1350   17      85\n"
     ]
    }
   ],
   "source": [
    "desktop = pd.read_csv('desktop.csv')\n",
    "laptop = pd.read_csv('laptop.csv')\n",
    "\n",
    "print(desktop.head())\n",
    "print(laptop.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "188d3bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Goal: To determine whether the users described in the desktop and laptop dataframes differ from each other\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "sns.reset_orig()\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.set_title('Spending by Desktop and Laptop Subscribers')\n",
    "ax1.boxplot([desktop['spending'].values,laptop['spending'].values])\n",
    "ax1.set_ylabel('Spending ($)')\n",
    "plt.xticks([1,2], ['Desktop Subscribers','Laptop Subscribers'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e0fdc0",
   "metadata": {},
   "source": [
    "Interpretations:\n",
    "- Both have a minima at 0.\n",
    "- Laptop subs have a higher 25th, 50th, and 75th percentile.\n",
    "- Laptop subs have a high outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0555fca5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35.766666666666666\n",
      "38.733333333333334\n",
      "32.0\n",
      "33.0\n",
      "957.5\n",
      "1528.25\n",
      "14.943634839697543\n"
     ]
    }
   ],
   "source": [
    "# Summary Statistics\n",
    "print(np.mean(desktop['age']))\n",
    "print(np.mean(laptop['age']))\n",
    "\n",
    "print(np.median(desktop['age']))\n",
    "print(np.median(laptop['age']))\n",
    "\n",
    "print(np.quantile(laptop['spending'],.25))\n",
    "print(np.quantile(desktop['spending'],.75))\n",
    "\n",
    "print(np.std(desktop['age']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "903ed583",
   "metadata": {},
   "source": [
    "Hypothesis 0  The two email lists are random samples from the same population.\n",
    "\n",
    "Hypothesis 1  The two email lists are not random samples from the same population."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2146ecc5",
   "metadata": {},
   "source": [
    "null hypothesis -> describes a world in which there's a population of people who are interested in desktops AND laptops\n",
    "- this category of people sign up for company's email lists occasionally\n",
    "- they choose the list option randomly\n",
    "\n",
    "alternate hypothesis -> describes a world in which the null hypothesis is not true -> differences in people's taste of desktops vs laptops"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efc6a146",
   "metadata": {},
   "source": [
    "“If Hypothesis 0 is true, it would be reasonable to send the same marketing email to both groups. \n",
    "\n",
    "If Hypothesis 1 is true, sending different marketing emails to each group makes more sense.”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aa06838f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-2.109853741030508, pvalue=0.03919630411621095)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.ttest_ind(desktop['spending'],laptop['spending'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56d6d08e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0.04 < 0.05, reject null hypothesis"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}