ABTEST.ipynb

{
 "metadata": {
  "name": "ABTEST "
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A/B Testing\n",
      "-----\n",
      "\n",
      "I think there are mainly two questions to be answered here. \n",
      "Q1. Are the data reliable/consistent? \n",
      "Q2. Is the A/B test significant?\n",
      "\n",
      "Exploring the data set \n",
      "------\n",
      "\n",
      "Let's first read in the data. I am using the Pandas library to do my data wrangling. I use the `describe()` function to get the basic statistics from each numeric column in the data.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "logs= pd.read_csv('./takehome.csv')\n",
      "logs.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ts</th>\n",
        "      <th>converted</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>count</strong></td>\n",
        "      <td> 1.911480e+05</td>\n",
        "      <td> 191148.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>mean</strong></td>\n",
        "      <td> 1.357042e+09</td>\n",
        "      <td>      0.101100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>std</strong></td>\n",
        "      <td> 2.494477e+04</td>\n",
        "      <td>      0.301461</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>min</strong></td>\n",
        "      <td> 1.356998e+09</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>25%</strong></td>\n",
        "      <td> 1.357020e+09</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>50%</strong></td>\n",
        "      <td> 1.357042e+09</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>75%</strong></td>\n",
        "      <td> 1.357063e+09</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>max</strong></td>\n",
        "      <td> 1.357085e+09</td>\n",
        "      <td>      1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "                 ts      converted\n",
        "count  1.911480e+05  191148.000000\n",
        "mean   1.357042e+09       0.101100\n",
        "std    2.494477e+04       0.301461\n",
        "min    1.356998e+09       0.000000\n",
        "25%    1.357020e+09       0.000000\n",
        "50%    1.357042e+09       0.000000\n",
        "75%    1.357063e+09       0.000000\n",
        "max    1.357085e+09       1.000000"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I find there are 191148 rows in the table. There are no NULL values in the data set which is a good thing. \n",
      "\n",
      "\n",
      "Let us look at the first 10 rows of the table, to get a sense of its contents "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "logs.head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>user_id</th>\n",
        "      <th>ts</th>\n",
        "      <th>ab</th>\n",
        "      <th>landing_page</th>\n",
        "      <th>converted</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td>  9636490229</td>\n",
        "      <td> 1356998400</td>\n",
        "      <td> treatment</td>\n",
        "      <td> new_page</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td>  2456307036</td>\n",
        "      <td> 1356998400</td>\n",
        "      <td> treatment</td>\n",
        "      <td> new_page</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>  9672320886</td>\n",
        "      <td> 1356998400</td>\n",
        "      <td>   control</td>\n",
        "      <td> old_page</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td>  2249836163</td>\n",
        "      <td> 1356998401</td>\n",
        "      <td> treatment</td>\n",
        "      <td> new_page</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 781266x9934</td>\n",
        "      <td> 1356998401</td>\n",
        "      <td>   control</td>\n",
        "      <td> old_page</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td>  1964424576</td>\n",
        "      <td> 1356998401</td>\n",
        "      <td>   control</td>\n",
        "      <td> old_page</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>6</strong></td>\n",
        "      <td>  2120441771</td>\n",
        "      <td> 1356998401</td>\n",
        "      <td>   control</td>\n",
        "      <td> old_page</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>7</strong></td>\n",
        "      <td>  3674211835</td>\n",
        "      <td> 1356998401</td>\n",
        "      <td> treatment</td>\n",
        "      <td> new_page</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>8</strong></td>\n",
        "      <td>  3674211835</td>\n",
        "      <td> 1356998402</td>\n",
        "      <td> treatment</td>\n",
        "      <td> old_page</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>9</strong></td>\n",
        "      <td>  2936816600</td>\n",
        "      <td> 1356998404</td>\n",
        "      <td>   control</td>\n",
        "      <td> old_page</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "       user_id          ts         ab landing_page  converted\n",
        "0   9636490229  1356998400  treatment     new_page          0\n",
        "1   2456307036  1356998400  treatment     new_page          0\n",
        "2   9672320886  1356998400    control     old_page          0\n",
        "3   2249836163  1356998401  treatment     new_page          0\n",
        "4  781266x9934  1356998401    control     old_page          0\n",
        "5   1964424576  1356998401    control     old_page          1\n",
        "6   2120441771  1356998401    control     old_page          1\n",
        "7   3674211835  1356998401  treatment     new_page          0\n",
        "8   3674211835  1356998402  treatment     old_page          0\n",
        "9   2936816600  1356998404    control     old_page          0"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A/B test function \n",
      "----\n",
      "\n",
      "Lets make a litte function that would return the mean $p$ and the standard error $SE$  given conversion number and sample size. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def abFunction(conversions, sampleSize):\n",
      "    p = conversions/float(sampleSize)\n",
      "    q = 1. - p \n",
      "    se = np.sqrt(float(p)*float(q)/float(sampleSize))\n",
      "    return p, se                            "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data Integrity \n",
      "----\n",
      "\n",
      "In an A/B test each user is either assigned to the control or to the treatment but not both. And each user should appear only once or if repeated the page seen by the user should be consistent. \n",
      "Looking at the first 10 rows above we already see that indices 7 and 8 have the same `user_id`. Also for index 8, `old_page` is wrongly associated with `treatment`. Lets find out how many \n",
      "are repeated like this: \n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lv = logs.user_id.value_counts()\n",
      "lv[lv.values >1 ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "9999097208    2\n",
        "9999086678    2\n",
        "9996999954    2\n",
        "9995913068    2\n",
        "998869147     2\n",
        "9987587119    2\n",
        "998758641     2\n",
        "9983262704    2\n",
        "9979926846    2\n",
        "9979567919    2\n",
        "9978866081    2\n",
        "9977974517    2\n",
        "997611340     2\n",
        "9975967743    2\n",
        "9973092221    2\n",
        "...\n",
        "1028822442    2\n",
        "1024693190    2\n",
        "1024644992    2\n",
        "1018796081    2\n",
        "1018493716    2\n",
        "1017331721    2\n",
        "1016622295    2\n",
        "1016121649    2\n",
        "1009139461    2\n",
        "1008531502    2\n",
        "1007157047    2\n",
        "1006100927    2\n",
        "1002929560    2\n",
        "1001922884    2\n",
        "1001572125    2\n",
        "Length: 4764"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So the above ids have been repeated, which is probably due to mislabeling on the server/client side (they usually have consequtive time stamps too). \n",
      "There are 4764/2 = 2382 user_ids that have been repeated. \n",
      "\n",
      "\n",
      "Let's drop the duplicates, to be conservative: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "logs_cleaned = logs.drop_duplicates(cols=['user_id'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "logs_cleaned"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 186384 entries, 0 to 191147\n",
        "Data columns:\n",
        "user_id         186384  non-null values\n",
        "ts              186384  non-null values\n",
        "ab              186384  non-null values\n",
        "landing_page    186384  non-null values\n",
        "converted       186384  non-null values\n",
        "dtypes: float64(1), int64(1), object(3)"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lets separate out the control and the treatment groups."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "control = logs_cleaned[logs_cleaned.ab == 'control']\n",
      "treatment = logs_cleaned[logs_cleaned.ab == 'treatment']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "control.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "(90811, 5)"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "treatment.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "(95573, 5)"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After weeding out the duplicates, we have 90811 control cases and 95573 treatment cases, which is slightly skew, but still roughly a 50/50 split, and suitable for A/B testing. \n",
      "\n",
      "Let's make sure that the control group has only `old_page` as the landing page, while the treatment group has only `new_page`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "control.landing_page.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "array([old_page], dtype=object)"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "treatment.landing_page.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "array([new_page], dtype=object)"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That looks good. Now, we can proceed to do the A/B testing. \n",
      "\n",
      "\n",
      "Computing the mean and standard errors of control and treatment groups \n",
      "----\n",
      "\n",
      "Our Null Hypothesis here is there is no difference between the control and treatment group. \n",
      "Our hope it to be able to reject the null hypothesis i.e. to say with enough confidence that the variation did lead to a signifant change in the conversion rate. To do this, we need to compare  the sample means of the treatment and control groups and test whether they are significantly different. To do that, we compute the 95% confidence interval of each mean (the interval within which the mean can randomly occur 95% of times the experiment is repeated).  If the two intervals are non-overlapping, then we can be sure beyond reasonable doubt that there is a real effect in the treatment group.  The confidence interval is defined as $(M \\pm 1.96\\times {SE})$ where  $M$ is sample mean, and $SE$ is the standard error. The asuumption here is that the sample mean is distributed as a normal variable, which is a very good approximation for a large sample such as this one. \n",
      "\n",
      "\n",
      "\n",
      "First,  we compute the sample mean $p$ and the confidence interval for the control group:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p_c, se_c = abFunction(control.converted.sum(), control.shape[0])\n",
      "print \"The control group conversion rate is (%f +/- %f) percent.\"%(p_c*100., se_c*1.96*100.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The control group conversion rate is (10.041735 +/- 0.195484) percent.\n"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, we do the same for the treatment group:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p_t, se_t = abFunction(treatment.converted.sum(), treatment.shape[0])\n",
      "print \"The treatment group conversion rate is (%f +/- %f) percent.\"%(p_t*100., se_t*1.96*100.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The treatment group conversion rate is (10.139893 +/- 0.191377) percent.\n"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Therefore, we see that the confidence interval for the conversion rate in the control is $(10.04 \\pm 0.19)$. While the same for the treatment sample is  $(10.14 \\pm 0.19)$. Therefore, although there seems to be an improvement in the conversion in the mean, it is not significant at all, the the intervals overlap a lot. So *we cannot conclude yet* that the variation lead to an improved conversion rate. \n",
      "\n",
      "\n",
      "Another way of seeing this is to compute the $Z$-score: \n",
      "\n",
      "Z-Score \n",
      "----\n",
      "\n",
      "$Z$ score computes the distance between the means of the control and treatment group in terms of the standard errors of each sample:\n",
      "\n",
      "$$ z = \\frac{(M_1 - M_2)}{(SE_1^2 + SE_2^2)^{1/2}} $$\n",
      "\n",
      "Ideally for $95\\%$ confidence (two-tailed) we would like the have a $Z$ value of 1.96 (1.645 for one-tailed). \n",
      "\n",
      "The probabilty of the two means being different by chance is given by:\n",
      "\n",
      "\n",
      "$ 1. - \\rm{Probabilty}(\\lt z) $ if $\\rm{Probabilty}(\\lt z) \\ge 0.5$\n",
      "\n",
      "or \n",
      "\n",
      "$\\rm{Probabilty}(\\lt z)$ if $\\rm{Probabilty}(\\lt z) \\lt 0.5$\n",
      "\n",
      "Lets compute our Z score:\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Z_score = (p_t - p_c)/sqrt(se_c**2+se_t**2)\n",
      "Z_score"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "0.7032630087978613"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lets compute the probability:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.stats\n",
      "proba = scipy.stats.norm.cdf(Z_score)\n",
      "print \"Chance of being this different randomly %f\"%(1. - proba)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Chance of being this different randomly 0.240946\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Therefore, we see that Z-score is only 0.7, which means that there is a $24\\%$ chance that the means are different just due to random fluctuations. Hence, the difference is not significant at all. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How large a sample do we need?\n",
      "-----\n",
      "\n",
      "The next obvious question is how large a test sample we would need to make a significant detection. An easy way to estimate this is to say that both groups have roughly the same $SE = SE_1 \\simeq SE_2$, and then demand for a Z-score of 1.96. This gives, from the definition of z:\n",
      "\n",
      "\n",
      "$$ 1.96 = \\frac{(M_1 - M_2)}{(2 ~ SE^2)^{1/2}} $$ \n",
      "\n",
      "Remembering that $SE^2 = p~(1-p)/N$, we have:\n",
      "\n",
      "\n",
      "$$ N = 2\\times 1.96^2  p~(1-p) /(M_1 -M_2)^2$$ per branch (either control or treatment).\n",
      "\n",
      "\n",
      "Let's compute  this number:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diff = 0.001\n",
      "se0  = sqrt(0.10*(1.-0.10))\n",
      "N = (np.sqrt(2)*1.96*se0/diff)**2\n",
      "print N"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "691488.0\n"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Therefore, in each branch we need 691488 samples, meaning that in total for a 50/50 split we need twice this value i.e. 1382976 samples. Currently, we only have about 191000 samples which is clearly 6 times smaller than what we need.  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some Visuals\n",
      "-------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pylab.figure(figsize=(12,8))\n",
      "\n",
      "running_num_c = (numpy.ones(control.shape[0]).cumsum())\n",
      "running_p_c = (control.converted.cumsum()/running_num_c).values#*100.\n",
      "running_se_c = np.sqrt(running_p_c*(1.-running_p_c)/running_num_c)*1.96#*100\n",
      "\n",
      "\n",
      "\n",
      "running_num_t = numpy.ones(treatment.shape[0]).cumsum()\n",
      "running_p_t = (treatment.converted.cumsum()/running_num_t).values#*100.\n",
      "running_se_t = np.sqrt(running_p_t*(1.-running_p_t)/running_num_t)*1.96#*100.\n",
      "\n",
      "pylab.plot(control.ts, running_p_c,color='red')\n",
      "pylab.fill_between(control.ts.values, running_p_c - running_se_c,running_p_c + running_se_c,alpha=0.3,color='red',edgecolor='red')\n",
      "\n",
      "pylab.plot(treatment.ts, running_p_t)\n",
      "pylab.fill_between(treatment.ts.values, running_p_t - running_se_t,running_p_t + running_se_t,alpha=0.3)\n",
      "\n",
      "pylab.ylim(9.0/100.,11./100.)\n",
      "pylab.xlim(treatment.ts.min(), treatment.ts.max())\n",
      "pylab.ylabel(\"Conversion Rate\",fontsize=20)\n",
      "pylab.xlabel(\"Time-stamp\",fontsize=20)\n",
      "pylab.legend(['Control','Treatment'])\n",
      "pylab.title(\"Conversion rate with confidence bands over time\", fontsize=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "<matplotlib.text.Text at 0x1154b9cd0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAIECAYAAADB80IoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCYNvIiIiIqIwYfBNRERERBQm/w8DrtYx4ckSDwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11553a8d0>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p_cc = p_c*100.\n",
      "p_tt = p_t*100. \n",
      "se_cc = se_c*100 \n",
      "se_tt = se_t*100 \n",
      "dd = {'control':[p_cc], 'treatment':[p_tt]}\n",
      "df3 = pd.DataFrame(dd)\n",
      "df3.plot(kind='bar',yerr=[[1.96*se_cc], [1.96*se_tt]],color=['gray','teal'],ecolor='black')\n",
      "pylab.ylim(9.8,10.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "(9.8, 10.5)"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD6CAYAAABebNdxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAcAg/wvP/pUoUspPSAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1154e52d0>"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Exceutive Summary\n",
      "======\n",
      "\n",
      "* The data in the logs are not readily reliable as I found mislabeled entries (`old_page` labeled as `treatment` and repeated `user_ids`. \n",
      "* After cleaning the logs, we find that the mean of the control sample is 10.04% while the mean of the treatment is 10.14% with a difference of 0.1%. However, given the sample size, we deduce that if the experiment were to be repeated, the observed mean could randomly lie anywhere in the range $(10.04 \\pm 0.19) \\%$ in $95\\%$ of the trails. Similarly, the treatment mean could lie anywhere in the range $(10.14 \\pm 0.19) \\%$. \n",
      "* The above numbers clearly show that these intervals overlap largely, and there is NO clear winner at this stage. \n",
      "* This is shown graphically in the above two figures. The first figure shows the coversion rate as a function of time, along with the 95% confidence interval shown as a band. As it shows, the trend is only beginning to show, but the two curves are well within each others confidence intervals, and there is no clear distinction at this point.  \n",
      "* The final means of the control and treatment samples are again shown as bar charts in the final figure, with there expected spread shown with error bars. Here again, we see that within the region where the means can randomly fluctuate, there is no clear distinction to be made between the two cases. \n",
      "* Finally, we conclude that we should not have stopped the experiment, but continued it until we had about 1382976 visitors split 50/50 between control and treatment. \n",
      "* Also, the data collection needs to be done more consistently avoiding wrong labeling. \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "def css_styling():\n",
      "    styles = open(\"./custom.css\", \"r\").read()\n",
      "    return HTML(styles)\n",
      "css_styling()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
        "    }\n",
        "    div.cell{\n",
        "        width:800px;\n",
        "        margin-left:16% !important;\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    h1 {\n",
        "        font-family: Helvetica, serif;\n",
        "    }\n",
        "    h4{\n",
        "        margin-top:12px;\n",
        "        margin-bottom: 3px;\n",
        "       }\n",
        "    div.text_cell_render{\n",
        "        font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
        "        line-height: 145%;\n",
        "        font-size: 130%;\n",
        "        width:800px;\n",
        "        margin-left:auto;\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    .CodeMirror{\n",
        "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
        "    }\n",
        "    .prompt{\n",
        "        display: None;\n",
        "    }\n",
        "    .text_cell_render h5 {\n",
        "        font-weight: 300;\n",
        "        font-size: 16pt;\n",
        "        color: #4057A1;\n",
        "        font-style: italic;\n",
        "        margin-bottom: .5em;\n",
        "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "    }\n",
        "    \n",
        "    .warning{\n",
        "        color: rgb( 240, 20, 20 )\n",
        "        }  \n",
        "</style>\n",
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
        "                           extensions: [\"AMSmath.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
        "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
        "                }\n",
        "        });\n",
        "</script>"
       ],
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "<IPython.core.display.HTML at 0x11312ff10>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}