4. AB Testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"https://raw.githubusercontent.com/DataForScience/Networks/master/data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\" width=150px> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> \n",
    "        <h1>Applied Probability Theory From Scratch</h1>\n",
    "        <h1>A/B Testing</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "        @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import scipy\n",
    "from scipy import stats\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.5\n",
      "IPython version      : 7.19.0\n",
      "\n",
      "Compiler    : Clang 10.0.0 \n",
      "OS          : Darwin\n",
      "Release     : 21.4.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: 967e6d5ea0aa8fdb11b3a19bce642a60fcedbb9a\n",
      "\n",
      "matplotlib: 3.3.2\n",
      "pandas    : 1.1.3\n",
      "json      : 2.0.9\n",
      "seaborn   : 0.11.0\n",
      "numpy     : 1.19.2\n",
      "watermark : 2.1.0\n",
      "scipy     : 1.6.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the default style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('./d4sci.mplstyle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(N, pA, pB, fraction=0.5):\n",
    "    df = pd.DataFrame({'group': (np.random.random(N)<(1-fraction)).astype('int'), 'conversion': 0})\n",
    "    counts = df.groupby('group').count()\n",
    "    \n",
    "    NA = counts.loc[0, 'conversion']\n",
    "    NB = counts.loc[1, 'conversion']\n",
    "    \n",
    "    df.loc[df['group']==0, 'conversion'] = (np.random.random(NA) < pA).astype('int')\n",
    "    df.loc[df['group']==1, 'conversion'] = (np.random.random(NB) < pB).astype('int')    \n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's generate some random data. We're assuming two groups of users (chosen at random with probability $1/2$). The first group (control) has a conversion probability $p_A=5\\%$ and the new group has a conversion probability $p_B=5.5\\%$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pA_theory = 0.05\n",
    "pB_theory = 0.06\n",
    "N = 2000\n",
    "data = get_data(N, pA_theory, pB_theory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the \"data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>conversion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group  conversion\n",
       "0      1           0\n",
       "1      0           0\n",
       "2      0           0\n",
       "3      0           0\n",
       "4      1           1\n",
       "5      1           0\n",
       "6      0           0\n",
       "7      1           0\n",
       "8      1           0\n",
       "9      0           0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group averages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "conversion=1 means that they bought our product, while conversion=0 means they didn't. We can cheack how many users we have in each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>conversion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       conversion\n",
       "group            \n",
       "0             983\n",
       "1            1017"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = data.groupby('group').count()\n",
    "counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the fraction that converted within each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>conversion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.034588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.064897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       conversion\n",
       "group            \n",
       "0        0.034588\n",
       "1        0.064897"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means = data.groupby('group').mean()\n",
    "means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The conversion fractions are different from the theoretical ones. We can assume that each conversion is an independent process (similar to a coin flip), in which case, we expect that the observed values follow a Binomial distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcf9085ab50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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fzsSUezMAAAAAAIBxIZDJ2CullCSvTvLmJF+c5KZL+PLZUsr7kvxxkt+uquqx5kc4vkop1yf5qly87i/PxfBlv1+7Mxev++8neU9VVSPV3sHPHQCMrl3Hevlv93Ubrbl2WfLtL5tIu2XzHQBzp3umyp5f7ubIOwe/6Xz1a1u55p+2U9rmumfzdS9oZ/+pKh/aO5iljn0nk+/5s9l860snctdGnbkBAJKktbhk03e2s+S6kv2/1uy6zzOqksPv6OX4Pb1s+IZ2Vr68lYsfHwEAAAAAADDK7MhhbJVSFpdSvjkXOw6+K8m35tJCccnFAOGrk/xkkodLKW8rpbyi2ZGOn1LKK0opb0vyUC5euy/IJYQxn7I5F//O3pVkaynlm0spixsd6AD4uQOA0Xb6QpWfeH8nFxrclzfRSr7r5ROZXmzDHQBz5+THetn+bbNzEsZc9yWtXPPNwpj9ardKvv1lE9m0cnDX61wn+fH3dfLrH++kN1rvuAIAGJhSSq78W+1c/70TaS2Zu/N2jiW7fqqbR/5dJ6e2zU2HTgAAAAAAAAZHIJOxVEp5Q5JPJPm5JLc2VLaV5MuT3F1K+Y1SytUN1R0bpZQNpZTfSHJ3Ll6rpn7H3JqLf5efKKW8vqGajfNzBwCjraqq/Nd7O9l3stm633hHO5vXePQCYG50z1Z54n908vAPdTJ7aPDnW//VrVz9j9opukBfkqWTJd//yomsHHAQ4Dc/2cuPv6+TM7NCmQAAn7LizlY2/9hEFl0xt+c9+UCVh76/k53fN5vj9/VS9dyjAQAAAAAAjCK7ghkrT3Un/IUkb8+ldyW8FF+dZHsp5SsHeI6RUkr5qiTbcvHaDMpNSf6klPILw9Qt088dAIyH39nWyz27m90I9wXXtfJFN3rsAmBunPpkLw9+x2wOv2Nuuu5c/fXtXPV3J1KKMOblWDtV8q9fOZFF7cGe5749Vb7nz2az54QN/wAAn7J0Uys3/8Rkpp4z9/eyp7dXefTHOnnw2zs58p5uel6eAQAAAAAAMFLsDGZsPNU58L1JvmGOTjmd5LdLKT9SFvDOw3LRjyT5rVy8JnPhG5L8RSnlqjk63zPycwcAo+98p8rP3tPJr36s22jdTStLvunFbSEVAAaud77K7l/o5KF/28mFJ+fghCW55pvbueLNA04SLgCb17Tyva8YfChzz4nke/5sNvftmZuwLgDAKJiYKbnpByey5vXz85H5uSeqPP4z3Wz7Z7M58AfddM8KZgIAAAAAAIwCgUzGQinlyiR/nuTOuT51kh9I8tNzfN5h8l9y8RrMddLgriR/XkpZP8fn/St+7gBg9O05UeV7/qyTdz3abDhh2WTy3S+fyOIJYUwABuv09otdMQ/9US+Zi/3b7WTTt7ez5guFMZvygqta+cEvmMjKJYM9z5nZ5Mfu7uTH3zeb3bplAgAkScpEyTXfNJFN39nOxMz8jGH2cLL3l7rZ+k2z2ff/Oukcd68GAAAAAAAwzAQyGXmllNVJ3plk8zwO41tKKT86j+efF6WUH0vyL+dxCDcneedTPwNzys8dAIy+u3d1811/OpvHB7DJ7VvumshV08KYAAzOub1VHv/ZTnb+QCfn983NOcui5PrvmciqVwhjNm3LulZ+4osms3n14O8f7tld5dvePpufvbeTQ2ds9gcASJJVL2/n1p+ZzOrXzd/H591TyZO/1csnv2k2u/9XJ+efdK8GAAAAAAAwjCbmewBQRymlJPmVJM+5hC/r5WJXw99Jcl+Sx5IcTbI4yZokz0vy8iRfk2TTJdT9vlLKx6qq+o1L+JqRVUr5u0m+9xK/7NEk/y/J+5N8IsnhJLNJVia5Phc7TX5lks9P/x03n5vkV0opX1xV1Zx8Mu3nDgBG24VulV/6SDfveKjZrpif8pVbWrlzo3ffADAYZx7u5cnf6eb4B6u56Yj5lNaS5Prvm8j0c81xg7J2WcmPvHYi//ND3by74e7dn6lXJe96pJe7d/Xyps2tfMWWdqYXe5kEALCwTUyXXPvPJrL61b3s/u/dnHtifgKR1YXk0Nt7OfSOXla+vJX1X97K0uvdhwMAAAAAAAwLgUxG3fcmeeMlHP87Sb63qqqdT/O/zSY5lWRXkj8spXxfkq9O8mO5GBbsx/8qpdz/DPXHRinl5iT/8xK+5JEk35Pkrc8Qmjzw1J97kvxMKeWWJG9J8uV91n/jU/XfcgljqsPPHQCMqH0nq/zHD3Ty6NHBbKh77hUlX/M8XcMAaFZVVTn1iSoHfqebkw/M/abwiVXJDd87kWWbbQIftEXtkn9+Zzs3ri75xfu76Q74r/tCN/nd7b386cO9fMWWdr7k5lYWTwhmAgAL2/Itrdz8H0sO/n4v+3+rm+rCPA2klxy7u5djd/cy/YKSdV/SzvTtJaXtfg0AAAAAAGA+2UXFyCqlPDfJv+vz8DNJ/mFVVV/Vb2ituug3kjw/yW/2eZ7pJP/7qQ6KY+mp7+2Xc/F77cevJ3l+VVW/3W8Hy6qqHqyq6iuSfH2Ss32e50dKKZfSsfKy+LkDgNH1l0/08l1/OjuwMObqpcl3fN5E2i1TMgDNqHpVjn2wl53f28nDP9iZlzDm9B0lt/zkpDDmHCql5I2b2/mhV09kZvHcnPPMbPKrH+vmn/3hbN7xUDed3vx0gwIAGBatyZL1X9XOrT89mekXzv9az8mPVnnk33fyyW+czRP/o5NTn+ylcs8GAAAAAAAwL3TIZJT9TPr7GT6T5E1VVf3F5ZykqqpTSf5OKeVYkn/Sx5e8LMk/SPJ/Lud8I+DrcvF77MfPVlX1Ly73RFVV/e9SyhNJ/ijJkmc5fCIXfyZec7nn65OfOwAYMbPdKv/ngW7+aEdvYOdol+RfvXwiK5fM/wY9AEZfb7bK0ff2cuB3uzm/Z37G0FqabPhH7ax+bSve/zM/nnNFKz/5RZP5ifd18vCAXijxmY6eS/7Hh7r5/Qe7+XvPa+dl17TS8vcPACxgi68sueEHJnLsA73s+YVuOsfmdzydE8nhd/Ry+B29TK5OVn5eKytf0cqyzcV9OwAAAAAAwBzxantGUinlbyf5gj4P/5rLDcV9hn+a5Pf6PPbHSynLGzjnUHnqe3pLn4e/Ncm/rHvOqqreneRr+zz81aWUv1X3nM/Ezx0AjJ4Dp6p8/7s6Aw1jJsnXv7CdW9d6vAKgnu7ZKgf+oJtt/2w2T/zs/IUxlz+/5Nafnsya17Vt6p5n66ZK/v1rJ/L5183tfca+k8l/+kA33/OnnTywf7D3UQAAw66UklUvb2fLz0xm7RtbyZDcIs8eSQ7+YS87v7eTbd88m73/t5Mzj/ZSVTpnAgAAAAAADJIdw4yqf9PncT9dVdXvN3HC6uKnl/8oyRN9HH5lkn/cxHmHzDclWd/HcY8l+caqoU98q6r6nST/tc/Df6CJcz4DP3cAMELu3dPLd75jNg8dGewmtJdf28qbNnu0AuDydU5W2f8b3Wz9ptns/aVuZg/PzzhaS5KN39TOjT84kUXrhmSXOVk8UfItd7XzDS9spzXHfy0PH63yw3/eyQ+9ZzYPHRHMBAAWtvZUycZ/PJHNb5nIkuuG6375woHkwNt62fGdnWz/ltns+/VOzu0WzAQAAAAAABgEu4YZOaWUNyV5Xh+H7k7y/U2eu6qqo0m+vc/Dv72UMtnk+efTU9/Lt/V5+LdWVXW84SH86yT7+jju9lLKGxo+t587ABghnV6V//3RTt5ydyenZwd7ro0zyT97ie5hAFy6qlfl9IO97P75Trb+k9ns/41uuqfmbzxTzym55acms/b15rVhVErJl9zSzg9+wUSmF839+T/2ZJXv/tNO/s27ZvOOh7o5ed7mfgBg4Zra3MotPzmRq7++ndaS+R7NZzu/J3nyN3vZ/i2z2f7ts3nyrd2c3+/+DQAAAAAAoCkT8z0AuAzf0edx31dV1ZmmT15V1VtLKXcneeWzHHpNkq9O8qtNj2Ge/J0kG/s47j1NdYf8m6qqOlVK+YEkv9DH4d+Z5E8aHoKfOwAYAYdOV/lPf9nJg4cGv8lsyUTy3S+fzNJJoRUA+lN1qpz6ZJXj9/Ry7J5eOkfne0RJWZRc/Q/aWfvGVspct1/kkj1vfSs/+UWT+fH3dfLosbnfVP/Jg1U+ebCbn/9wN7dfWfLKTa3cuaHlfggAWHBKu+SKN7ez8mWt7P75Tk7cN5yBx3O7quzb1c2+X+1myTUly59bsvw5rSx/TsnECvdwAAAAAAAAl0Mgk5FSSrkmyWv6OPSJJP9vgEP58Tx7MC5Jvi7jE4z7+j6Pe8sAx/B/k/xIkquf5bjXlFI2VlW1u4mT+rkDgOFWVVV2HK7yJzt7ef8TvXR6gz/n0on/P3v3HSZbWtWL/7v6nDM5MQzMgMCQkaCSlCxZJSNBgoHkVfGqV/Qa70+vGa/pmuV6LwoCiooEiZKRoCJJJA5hBgaYIQyT4zmn1++P6onMTO063burquvzeZ5+Tp/utfe7qk6d2rX23ut9k1+43+7cyI1rAEyxfnHn3P/onPVv6znn39fnugrmVR1+m8qNf3R3Drmh49kyuf4Rld940O786bv35+2f3YYPPldjfyfvO63zvtP256Bd+3PXG67l3ieu5c43qBy0y+sJAFgdB12vcvOf25Oz/m09n3/uvuz9yrwzumYXndq56NTOV147+Qx5xQbNw29X2XOMz3EAAAAAAABDaMhk2Tw5yZCrgX/c3ftGzOM1SU5KcuspcQ+oqhO6+/QRcxldVd0gyf0HhH6ku18/Vh7dvbeq/iTJr08JXUvypCS/vUVDe90BwAK6eF/nHZ9dz2s/sZ5Pn7l9qxAce2jyM/fenVtdd23bxgRguew/v3PO+9Zz1r+u59z3d9YvmndGV1Z7khs8aVeu94i1lOa5pXTw7sqP32NXbn5s5QX/sT/rc1yQ6ZL9ybtOXc+7Tl3PYXuSu91oLfe5yVq+4fjKLquuAgAr4pi7reWoO+7JGW9Yz5f+cf9CN2Ze6mo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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "ax.set_xlabel('z')\n",
    "ax.set_ylabel('PMF')\n",
    "\n",
    "x = np.arange(0, N*pB_theory*1.5)\n",
    "yA = stats.binom(n=counts.iloc[0, 0], p=means.iloc[0, 0]).pmf(x)\n",
    "ax.plot(x, yA, label=r'$p_A=%1.3f$' % means.iloc[0])\n",
    "\n",
    "yB = stats.binom(n=counts.iloc[1, 0], p=means.iloc[1, 0]).pmf(x)\n",
    "ax.plot(x, yB, label=r'$p_B=%1.3f$' % means.iloc[1])\n",
    "\n",
    "height = np.max([yA.max(), yB.max()])*1.05\n",
    "\n",
    "ax.vlines(x=[pA_theory*counts.iloc[0], \n",
    "             pB_theory*counts.iloc[1]], ymin=0, ymax=height, \n",
    "          colors=colors[:2], linestyles='-.')\n",
    "ax.set_ylim(0, height)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This figure clearly illustrates that the observed number of conversions can be significantly differnet from the one we might expect theoretically (as represented by the dashed lines). Indeed, we can take the observed value as an \"experimental measurement\" of the true value. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the central limit theorem, we know that the difference between the true and the experimental values follows a gaussian distribution centered at zero and with a standard deviation given by the standard error of the meaurement. For a Bernoulli process, the standard error is given by"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$SE=\\sqrt{\\frac{p\\left(1-p\\right)}{N}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can best represent our experimental measurement as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcfa0b61d00>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "ax.set_xlabel('z')\n",
    "ax.set_ylabel('PMF')\n",
    "\n",
    "x = np.linspace(0, .15, 1000)\n",
    "pA = means.iloc[0]\n",
    "NA = counts.iloc[0]\n",
    "SEA = np.sqrt(pA*(1-pA)/NA)\n",
    "yA = stats.norm(pA, SEA).pdf(x)\n",
    "ax.plot(x, yA, label=r'$p_A=%1.3f$' % pA)\n",
    "\n",
    "pB = means.iloc[1]\n",
    "NB = counts.iloc[1]\n",
    "SEB = np.sqrt(pB*(1-pB)/NB)\n",
    "yB = stats.norm(pB, SEB).pdf(x)\n",
    "ax.plot(x, yB, label=r'$p_B=%1.3f$' % pB)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The corresponding Z score is then given by:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Z=\\frac{p_B-p_A}{\\sqrt{SE_A^2+SE_B^2}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zscore(pA, pB, NA, NB):\n",
    "    SEA = np.sqrt(pA*(1-pA)/NA)\n",
    "    SEB = np.sqrt(pB*(1-pB)/NB)\n",
    "    return ((pB-pA)/np.sqrt(SEA**2+SEB**2)).values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Z score for our example is then:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1321148238925325\n"
     ]
    }
   ],
   "source": [
    "Z = zscore(pA, pB, NA, NB)\n",
    "print(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know that Z scores follow a normal distribution centered at zero and unity width. We can use this to calculate the p-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pvalue(zscore=2):\n",
    "    return 1-stats.norm.cdf(zscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0008677599271369463\n"
     ]
    }
   ],
   "source": [
    "p = pvalue(Z)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get an intuitive view of the meaning of the p-value by plotting it in the z-score curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_zscore(zscore=2):\n",
    "    colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "    norm = stats.norm()\n",
    "    \n",
    "    x = np.linspace(-5, 5, 1000)\n",
    "    y = norm.pdf(x)\n",
    "    height = y.max()\n",
    "    \n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    ax.plot(x, y)\n",
    "    ax.set_xlabel('z')\n",
    "    ax.set_ylabel('PDF')\n",
    "    \n",
    "    p = pvalue(zscore)\n",
    "\n",
    "    ax.vlines(zscore, 0, height, color=colors[1], linestyle='--')\n",
    "    ax.text(zscore*1.05, height, \"z=%1.2f\" % zscore, va=\"top\", ha=\"left\")\n",
    "    ax.text(zscore*1.05, height*.9, \"p=%1.2g\" % p, va=\"top\", ha=\"left\")\n",
    "\n",
    "    selected = x > zscore\n",
    "    \n",
    "    ax.fill_between(x, 0, y, color=colors[2], alpha=0.4, where=selected)    \n",
    "    ax.text(0, height/2, np.round(1-p, 5), ha='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_zscore(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "title_cell": "Table of Contents",
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   "toc_position": {},
   "toc_section_display": true,
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  "varInspector": {
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   "types_to_exclude": [
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}