"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:23.400189Z",
"start_time": "2021-04-01T04:02:23.176391Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# code for loading the format for the notebook\n",
"import os\n",
"\n",
"# path : store the current path to convert back to it later\n",
"path = os.getcwd()\n",
"os.chdir(os.path.join('..', 'notebook_format'))\n",
"from formats import load_style\n",
"load_style(plot_style = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Warming-up"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:25.061827Z",
"start_time": "2021-04-01T04:02:23.769063Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ethen 2021-03-31 21:02:25 \n",
"\n",
"CPython 3.6.4\n",
"IPython 7.15.0\n",
"\n",
"numpy 1.18.5\n",
"scipy 1.4.1\n",
"pandas 1.0.5\n",
"matplotlib 3.1.0\n",
"statsmodels 0.11.1\n"
]
}
],
"source": [
"os.chdir(path)\n",
"\n",
"# 1. magic for inline plot\n",
"# 2. magic to print version\n",
"# 3. magic so that the notebook will reload external python modules\n",
"# 4. magic to enable retina (high resolution) plots\n",
"# https://gist.github.com/minrk/3301035\n",
"%matplotlib inline\n",
"%load_ext watermark\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import scipy.stats as stats\n",
"import matplotlib.pyplot as plt\n",
"from statsmodels.stats.proportion import proportions_ztest\n",
"from statsmodels.stats.proportion import proportions_chisquare\n",
"\n",
"%watermark -a 'Ethen' -d -t -v -p numpy,scipy,pandas,matplotlib,statsmodels"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:25.092513Z",
"start_time": "2021-04-01T04:02:25.065345Z"
}
},
"outputs": [],
"source": [
"# setup the look and feel of the notebook\n",
"plt.rcParams['figure.figsize'] = 8, 6\n",
"sns.set_context('notebook', font_scale = 1.5, rc = {'lines.linewidth': 2.5})\n",
"sns.set_style('whitegrid')\n",
"sns.set_palette('deep')\n",
"\n",
"# Create a couple of colors to use throughout the notebook\n",
"red = sns.xkcd_rgb['vermillion']\n",
"blue = sns.xkcd_rgb['dark sky blue']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ideally, the reader should already understand or vaguely remember the statistic concepts such as z-score, p-value, hypothesis test, confidence interval. The warming-up section is a quick review of the concept, feel free to skip it if you're already acquainted with the concept.\n",
"\n",
"Statistical inference is the process of analyzing sample data to gain insight into the population from which the data was collected and to investigate differences between data samples. In data analysis, we are often interested in the characteristics of some large population, but collecting data on the entire population may be infeasible. For example, leading up to U.S. presidential elections it could be very useful to know the political leanings of every single eligible voter, but surveying every voter is not feasible. Instead, we could poll some subset of the population, such as a thousand registered voters, and use that data to make inferences about the population as a whole."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Point Estimate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Point estimates are estimates of population parameters based on sample data. For instance, if we wanted to know the average age of registered voters in the U.S., we could take a survey of registered voters and then use the average age of the respondents as a point estimate of the average age of the population as a whole. The average of a sample is known as the sample mean.\n",
"The sample mean is usually not exactly the same as the population mean. This difference can be caused by many factors including poor survey design, biased sampling methods and the randomness inherent to drawing a sample from a population. Let's investigate point estimates by generating a population of random age data and then drawing a sample from it to estimate the mean:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:25.354580Z",
"start_time": "2021-04-01T04:02:25.301323Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"population mean: 43.002372\n"
]
}
],
"source": [
"# generate some random number to serve as our population\n",
"np.random.seed(10)\n",
"population_ages1 = stats.poisson.rvs(loc = 18, mu = 35, size = 150000)\n",
"population_ages2 = stats.poisson.rvs(loc = 18, mu = 10, size = 100000)\n",
"population_ages = np.concatenate((population_ages1, population_ages2))\n",
"print('population mean:', np.mean(population_ages))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:25.382052Z",
"start_time": "2021-04-01T04:02:25.357565Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sample mean: 42.388\n"
]
}
],
"source": [
"np.random.seed(6)\n",
"sample_ages = np.random.choice(population_ages, size = 500)\n",
"print('sample mean:', np.mean(sample_ages))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The experiment tells us that we'd expect the distribution of the population to be a similar shape to that of the sample, so we can assume that the mean of the sample and the population should have the same value. Note that we can't say that they exactly match, but it's the best estimate we can make.\n",
"\n",
"The population mean is often denoted as $\\mu$, the estimated population mean as $\\hat{\\mu}$, mean of the sample $\\bar{x}$. So here we're basically saying $\\hat{\\mu} = \\bar{x}$, where we're using the sample mean to estimate the mean of the population and usually the larger the size of our sample, the more accurate our point estimator for the estimated population mean is going to be."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sampling Distributions and The Central Limit Theorem"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Many statistical procedures assume that data follows a normal distribution, because the normal distribution has nice properties like being symmetric and having the majority of the data clustered within a few standard deviations of the mean. Unfortunately, real world data is often not normally distributed and the distribution of a sample tends to mirror the distribution of the population. This means a sample taken from a population with a skewed distribution will also tend to be skewed."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:29.959328Z",
"start_time": "2021-04-01T04:02:26.735542Z"
}
},
"outputs": [
{
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"image/png": {
"height": 384,
"width": 746
}
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (12, 6))\n",
"plt.subplot(1, 2, 1)\n",
"plt.hist(population_ages)\n",
"plt.title('Population')\n",
"plt.subplot(1, 2, 2)\n",
"plt.hist(sample_ages)\n",
"plt.title('Sample')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot reveals the data is clearly not normal: instead of one symmetric bell curve, it has as bimodal distribution with two high density peaks. Because of this, the sample we drew from this population should have roughly the same shape and skew.\n",
"\n",
"The sample has roughly the same shape as the underlying population. This suggests that we can't apply techniques that assume a normal distribution to this data set, since it is not normal. This leads to our next topic, the **central limit theorem**.\n",
"\n",
"The central limit theorem is one of the most important results of probability theory and serves as the foundation of many methods of statistical analysis. At a high level, the theorem states the distribution of many sample means, known as a sampling distribution, will be normally distributed. This rule holds even if the underlying distribution itself is not normally distributed. As a result we can treat the sample mean as if it were drawn normal distribution. To illustrate, let's create a sampling distribution by taking 200 samples from our population and then making 200 point estimates of the mean:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:30.494666Z",
"start_time": "2021-04-01T04:02:29.961677Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"image/png": {
"height": 365,
"width": 488
}
},
"output_type": "display_data"
}
],
"source": [
"np.random.seed(10)\n",
"samples = 200\n",
"point_estimates = [np.random.choice(population_ages, size = 500).mean()\n",
" for _ in range(samples)]\n",
"\n",
"plt.hist(point_estimates)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The sampling distribution appears to be roughly normal, despite the bimodal population distribution that the samples were drawn from. In addition, the mean of the sampling distribution approaches the true population mean:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:30.523049Z",
"start_time": "2021-04-01T04:02:30.496517Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"-0.08440799999999626"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"population_ages.mean() - np.mean(point_estimates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To hit the notion home, Central Limit Theorem states that that if we collect \"a large number\" of different samples mean from the population, the sampling distribution, the distribution of the samples mean you collected, will approximately take the shape of a normal distribution around the population mean no matter what the orginal population distribution is.\n",
"\n",
"Knowing that the sampling distribution will take the shape of a normal distribution is what makes the theorem so powerful, as it is the foundation of concepts such as confidence intervals and margins of error in frequentist statistics."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Confidence Interval"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A point estimate can give us a rough idea of a population parameter like mean, but estimates are prone to error. A confidence interval is a range of values above and below a point estimate that captures the true population parameter at some predetermined confidence level. For example, if we want to have a 95% chance of capturing the true population parameter with a point estimate and a corresponding confidence interval, we'd set our confidence level to 95%. Higher confidence levels result in a wider confidence intervals.\n",
"\n",
"The interval is computed using the formula: \n",
"\n",
"$$\n",
"\\begin{align}\n",
"\\text{point estimate} \\pm z * SE\n",
"\\end{align}\n",
"$$\n",
"\n",
"Where\n",
"\n",
"- $z$ is called **critical value** and it corresponds to the **confidence level** that we chose. Critical value is number of standard deviations we'd have to go from a normal distribution's mean to capture proportion of the data associated with our desired confidence level. For instance, we know that roughly 95% of the data in a normal distribution lies within 2 standard deviations from the mean, so we could use 2 as the z-critical value for a 95% confidence interval (although it is more exact to get z-critical values with `stats.norm.ppf()`)\n",
"- $SE$ represents **standard error**. Generally, standard error for a point estimate is estimated from our data and computed using a formula. For example, the sample mean's standard error is $\\frac{s}{ \\sqrt{n} }$, where $s$ is standard deviation and $n$ is number of samples.\n",
"- $z * SE$ is called the **margin of error**.\n",
"- Note that this constructing confidence intervals framework can be easily adapted for any estimator that has a nearly normal sampling distribution. e.g. sample mean, two sample mean, sample proportion and two sample proportion (we'll later see). All we have to do this is change the way that we're calculating the standard error."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:30.554460Z",
"start_time": "2021-04-01T04:02:30.524982Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"z-critical value: 1.959963984540054\n",
"point esimate: 42.523\n",
"Confidence interval: (41.70306406882683, 43.34293593117317)\n"
]
}
],
"source": [
"np.random.seed(10)\n",
"sample_size = 1000\n",
"sample = np.random.choice(population_ages, size = sample_size)\n",
"sample_mean = sample.mean()\n",
"\n",
"confidence = 0.95\n",
"z_critical = stats.norm.ppf(q = confidence + (1 - confidence) / 2)\n",
"print('z-critical value:', z_critical) \n",
"\n",
"pop_stdev = population_ages.std()\n",
"margin_of_error = z_critical * (pop_stdev / np.sqrt(sample_size))\n",
"confint = sample_mean - margin_of_error, sample_mean + margin_of_error\n",
"print('point esimate:', sample_mean)\n",
"print('Confidence interval:', confint)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that the confidence interval we calculated captures the true population mean of 43.0023.\n",
"Let's create several confidence intervals and plot them to get a better sense of what it means to \"capture\" the true mean:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:31.386544Z",
"start_time": "2021-04-01T04:02:30.557753Z"
}
},
"outputs": [
{
"data": {
"image/png": 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EBDghWsC5Lly4YIzfeOMN9enTx4nRAM6XlZWlefPmad26dTad9/777xvNsr59+2ru3LmqUqWK8ftTp04pIiJC9+7d0/bt29WrVy+FhoaWaeyAo5Q0T7799ltlZWVJkjp16qS33nrLHuEBTrNp0yajARAYGKhly5YpJCSkwJwpU6Zo4sSJOnbsmB48eKA//vGP2rhx4xOvtXDhQqNZ9uqrr2rp0qUF7iK7cuWKRo8eraSkJB0+fFibN2/W0KFD7ffmgDJUlrly7949paSkSMpvhFWW2sKm/yiRzZs3KzU1VVL+RcuPm2WSNHjwYCOR0tPTtXLlSkeGCJQb5jvMWrZs6cRIAOdLTk7W8OHDbW4CnDt3Tl9//bUkqXbt2oqMjCzQLJPy73SeP3++cbx48eJSxws4Q0nzRCpYc1q1alWWYQHlwurVq43xhx9++EQDQJICAgK0fPlyBQUFSZLi4+MVHx9fYM7333+vzZs3S5J8fHz0ySefPLHkskGDBlq2bJk8PDwkScuXL1d2dnaZvh/AXsoqV6T/v7tMqlzXMzTMUCLmjWRHjx5d6LzRo0cbe8jExMQoLy/P7rEB5c3jAuPn58eyF1RaFotFCxYsUO/evY0PYoGBgVZf0JvrzogRIwrdnyw0NNRYAnDmzBlduXKllJEDjlPaPJEKXtQUtRcN4IoSEhKM/fyaNWtW5ENe/Pz8NGDAAOP46NGjBX4fExNj3I05cODAQlfCNG3aVN27d5ck3b59+4nXAcqjsswVqfLeAEDDDDZLTU01nuAXFBSk5s2bFzrX39/f6GTfunVLZ86ccUiMQHmRkZFhXLA3b95cbm5uTo4IcI6tW7cqKipKGRkZkqQOHTpoy5YtatKkiVXnx8XFGeOuXbsWOdf8+71795YgWsA5Spsn0v83zLy8vGw6D3AF5qeIW9NIrlevnjF+vDrmsUOHDhnj4uqKeXk/dQWuoCxzRaq8X8awhxlsZu4uP+22zh9r06aNjh07Jil/f5k2bdrYLTagvElISDBu3X/8bczt27d14sQJpaamysfHRw0aNFDbtm3l7s53GKj4goKCNGnSJA0cONDqBnJ6errReH722WfVsGHDIueba9Pp06dLHizgJCXJE0nKy8sz9s1s2rSpvL29lZaWphMnTuj69evy8PBQ7dq11b59ez3zzDP2Ch+wmz59+qh9+/a6deuWqlWrVuz827dvG2MfH58Cv7Plmoa6AldTlrkiFdzwv27dusrIyFB8fLyuXr2qnJwcBQcHq3379hXuSbI0zGCza9euGeO6desWO7927dpPPReoDMwb/nt6emrMmDH65ptvjAdmPBYUFKTx48dryJAh3IWGCik4OFjTpk3ToEGDnvpBrCjXr183lvRbU3dq1apljKk7cCWlyRMp/997enq68VqzZs3Sjh07jJ89VqVKFQ0bNkzjx4+Xr69vmcQOOIKnp6dq1apV4P/zRYmNjTXGjRs3NsYWi0V37tyRJFWvXr3Yi3zqClxNWeWKJD169Mj44vKFF17QRx99pOjoaN2/f/+Jv9m/f3/9/ve/V2BgYCnfQflAwww2M9+iGRwcXOz8mjVrGuPHhQmoLMzfXq5YsaLQeampqXr//ff173//W/Pnz5eXl5cjwgMcplevXiU+11x3nnvuuWLnU3fgqkqTJ1LBmrNv375C52VkZGj16tU6dOiQVq5caVVeAa5m165dxrI0Ly+vAssuzXfTWPPvv1q1aqpWrZrS0tJksViUkZHxxINnAFdVVK5I0sWLF5WTkyNJOnnypE6ePPnU18nOztb27dt15MgRrVixokJsC8D6H9jsf//7nzGuWrVqsfPNxcRisdglJqC8Mq/3l/KfKvvZZ5/p+PHjOn78uKKjo9WvXz/j97t27VJkZKSjwwTKNXPdsWYZmbk2UXdQmfy45rRp00aLFy9WXFyczpw5o507d2rs2LFGjly8eFHjxo1TZmamM8IF7CYpKUl//vOfjeOhQ4cW2NTfXBusXZ5MbUFFVFyuSAW/jJGkhg0bat68eTpw4IDOnDmj2NhYTZ48WdWrV5ckpaSk6O2339bdu3ft/wbsjIYZbGb+UGXNNyvm4sIHMlQmubm5unjxonE8e/ZsLVy4UK+88op8fX3l6+url19+WQsWLND06dONeevXr2d/DMDE1rpjnkPdQWVibpiFhYUpOjpaPXv2VGBgoLy9vdW4cWO9++67Wrt2rdEkOHv2rNauXeuskIEyd+fOHUVEROjevXuS8jcznzBhQoE5ttYViWsaVDzW5IpUsLZ06dJF27Zt04ABA/Tcc8/J29tbzz//vEaNGqUtW7YYSzGTk5O1aNEix7wRO6JhBpt5eHgYY1v3WmJvJlQm7u7uio2N1caNG7Vq1SoNGjSo0LnDhw/X66+/bhyvWbPGARECroG6A1jn448/1o4dO7RkyRLNmjVLnp5P330lJCRE77zzjnFMzUFFkZqaqpEjRyoxMVFS/ublixYtemKPstI+aInaAldnba5I0pQpU/T5559r2bJlWrhwYaF7bNavX18zZswwjrdu3aq0tDT7vAEHoWEGm5kT5PFjz4vy6NEjY8xaf1Q2gYGBeumll9SlS5di5/7qV78yxocPH7ZnWIBLsbXumOdQd1CZ+Pr6qkWLFurRo4e8vb2LnDt48GCjGX379m1dunTJESECdpOUlKShQ4cad/dXqVJFS5YsUYsWLZ6Ya64r1t4tZr6mKS6/gPLMllyR8vPlxRdf1M9+9jNj2WVhevbsqRo1akjKz63//Oc/ZRu8g9Ewg83MBcZcOApjvnDhSUxA4Vq3bm2Mf/jhB5f/RgYoK+a68/Dhw2Lnm2sTdQd4Ol9fX73wwgvGcVJSkvOCAUopPj5egwYN0vXr1yXl70u2fPlyvfbaa0+dX61aNWNsTV2RqC2oGGzNFVu5u7urZcuWxrGr1xYaZrBZUFCQMb5161ax881zzOcCKOjHt0CzoSyQz/zUS/OTzQpD3QGsY6471By4ql27dmnEiBH64YcfJEnVq1fX6tWri2wABAYGGssqrakrFovF+CLT39+fO8zgkkqSKyVRkWoLDTPYzPx42OTk5GLn37x50xibv8kEKoucnByrlpH9+I6yp+0hAFRG9evXl5eXlyTr6k5KSooxpu6gssnLy1N6erpVc811h5oDV7Rq1Sr97ne/Mz5n1a5dWxs2bNDLL79c5HnPPPOM6tSpI0m6e/dusTlDXYGrK2mumFm7+sU8z8/Pz7ZAyxkaZrBZkyZNjG9kzp49W+z8U6dOGePC1kUDFdGaNWvUsWNHtWzZUnPnzi12vvmJmrVr17b6MedARefp6amGDRtKyr+wKe72/pMnTxpj6g4qi1OnTqlLly5q1aqVBg4cWOz8zMxMXb161Th+nGOAq1i0aJHmz5+vvLw8SVLz5s21adMmNW7c2KrzmzZtaoyLezo5dQWurDS58t133yk0NFStWrVSly5dlJubW+w55muaRo0alTzwcoCGGWzm7++vtm3bSsr/pv/bb78tdO7du3eNAuTv769WrVo5JEagPAgKCtLdu3eVl5engwcPGkWqMDt37jTGnTp1snd4gEsxP0X2wIEDRc41/96aB24AFUGdOnWUmpqq7OxsJSYm6saNG0XO37t3r7HZeb169VSvXj1HhAmUidWrV2vp0qXGcadOnbR+/foCS/iLY64r33zzTZFzzXWlrJevAfZU2lypWbOmHjx4oKysLKWnpxe7if/p06eN1QA+Pj5G38BV0TBDifTu3dsYL168uNB5UVFRysrKkiT179+/1I9wBlxJ165dVbVqVUnSjRs39MUXXxQ69/z589q2bZtxPHToULvHB7iSPn36GONVq1YVunxm//79OnfunCSpWbNmatasmUPiA5zt8VOZpfxlmX/7298KnWuxWPTRRx8Zx9QcuJJjx4+k3QkAAAUoSURBVI7pww8/NI67deumqKgomzfi79Gjh7Hcf9OmTYXuZXbhwgXt3btXkhQQEKCuXbuWMHLAscoiV9zd3dWtWzfjuKjakp2drTlz5hjH4eHhxrWQq6J7gRL55S9/qVq1akmS9uzZo48//viJu2c2bdqkv//975KkqlWr6q233nJ4nIAz+fr66te//rVxPHPmTMXFxT0xLz4+XqNGjTKay+Hh4dyNCfxIs2bN1KNHD0n5dzdPnDjxiY1kT506palTpxrH48ePd2iMgLONHTvWGG/ZskVRUVFPfD67deuWRo0aZTwhrVGjRgVqFVCeZWZmasqUKcrJyZEktW3bVp988kmJNuEPCAjQsGHDJOXvuTRmzJgnmmZXrlzR+PHjjWVoERERbPgPl1CWuRIRESEPDw9JUlxcnGbPnq3s7OwCcx48eKCJEycqPj5eklSjRo0K8TnMLa+4NUJAIfbv369x48YZBaRp06bq1q2bPDw8dOTIEZ04ccKYO2vWLA0ePNhZoQJO8/DhQ7355psF9sbo3Lmz2rVrp7y8PJ08eVJxcXHGBc1LL72kVatWFXjcOVCRTZ06VTt27JAkrV27Vh06dCh07s2bNxUeHq67d+9Kyl/23KtXLwUEBOjChQvat2+f8QGuX79+WrBggf3fAOAAtuTJ7NmztX79euO4adOmCg0NVbVq1XT58mXt27fPuEOzRo0aWrduncvvMYPKY+PGjZo5c6ZxPGzYMGPz/uI0adLkibvDLBaLwsPDde3aNUn5D7/o3bu3atWqpStXrmjPnj169OiRJKl9+/b6xz/+IU9PzzJ6N4D9lHWurF69WvPmzTOO69atq+7du+snP/mJkpKStHfvXt27d09S/kM1Vq5cqVdeeaUM3olz0TBDqcTExGjGjBmFLo3x9PTU5MmTNXLkSMcGBpQj9+/f19SpU/XVV18VOa9Xr176y1/+4vJPkwFsYUsjQMpfGjNu3Lgin5bZr18/zZ07l7sAUGHYkie5ublatGiRPv300yI3Z27RooX++te/Wr1BOlAeDB48uMAG/LYICwt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"text/plain": [
"
"
]
},
"metadata": {
"image/png": {
"height": 474,
"width": 614
}
},
"output_type": "display_data"
}
],
"source": [
"np.random.seed(12)\n",
"confidence = 0.95\n",
"sample_size = 1000\n",
"\n",
"intervals = []\n",
"sample_means = []\n",
"for sample in range(25):\n",
" sample = np.random.choice(population_ages, size = sample_size)\n",
" sample_mean = sample.mean()\n",
" sample_means.append(sample_mean)\n",
"\n",
" z_critical = stats.norm.ppf(q = confidence + (1 - confidence) / 2) \n",
" pop_std = population_ages.std()\n",
" margin_error = z_critical * (pop_stdev / np.sqrt(sample_size))\n",
" confint = sample_mean - margin_error, sample_mean + margin_error \n",
" intervals.append(confint)\n",
" \n",
"\n",
"plt.figure(figsize = (10, 8))\n",
"plt.errorbar(x = np.arange(0.1, 25, 1), y = sample_means, \n",
" yerr = [(top - bot) / 2 for top, bot in intervals], fmt = 'o')\n",
"\n",
"plt.hlines(xmin = 0, xmax = 25,\n",
" y = population_ages.mean(), \n",
" linewidth = 2.0, color = red)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that in the plot above, all but one of the 95% confidence intervals overlap the red line marking the true mean. This is to be expected: since a 95% confidence interval captures the true mean 95% of the time, we'd expect our interval to miss the true mean 5% of the time.\n",
"\n",
"More formally, the definition of a 95% confidence interval means that **95% of confidence intervals, created based on random samples of the same size from the same population will contain the true population parameter**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hypothesis Testing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets starts off with a motivating example that asks the question \"If you toss a coin 30 times and see 22 heads, is it a fair coin?\"\n",
"\n",
"We all know that a fair coin should come up heads roughly 15 out of 30 tosses, give or take, so it does seem unlikely to see so many heads. However, the skeptic might argue that even a fair coin could show 22 heads in 30 tosses from time-to-time. This could just be a chance event. So, the question would then be \"how can you determine if we're tossing a fair coin?\"\n",
"\n",
"Let's start by first considering the probability of a single coin flip coming up heads and work our way up to 22 out of 30.\n",
"\n",
"$$\n",
"\\begin{align}\n",
"P(H) = \\frac{1}{2}\n",
"\\end{align}\n",
"$$\n",
"\n",
"As our equation shows, the probability of a single coin toss turning up heads is exactly 50% since there is an equal chance of either heads or tails turning up. Taking this one step further, to determine the probability of getting 2 heads in a row with 2 coin tosses, we would need to multiply the probability of getting heads by the probability of getting heads again since the two events are independent of one another.\n",
"\n",
"$$\n",
"\\begin{align}\n",
"P(HH) = P(H) \\cdot P(H) = P(H)^2 = \\left(\\frac{1}{2}\\right)^2 = \\frac{1}{4}\n",
"\\end{align}\n",
"$$\n",
"\n",
"Let's now take a look at a slightly different scenario and calculate the probability of getting 2 heads and 1 tails with 3 coin tosses. To get the actual probability of tossing 2 heads and 1 tails we will have to add the probabilities for all of the possible permutations, of which there are exactly three: HHT, HTH, and THH.\n",
"\n",
"$$\n",
"\\begin{align}\n",
"P(2H,1T) = P(HHT) + P(HTH) + P(THH) = \\frac{1}{8} + \\frac{1}{8} + \\frac{1}{8} = \\frac{3}{8}\n",
"\\end{align}\n",
"$$\n",
"\n",
"Another way we could do this is to use the binomial distribution:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"P(N_H,N_T) = \\binom{n}{k} p^{k} \\left( 1 - p \\right)^{n - k}\n",
"\\end{align}\n",
"$$\n",
"\n",
"Where \n",
"\n",
"- $n$ is number of coin flips\n",
"- $p$ is the probability of getting heads on each flip\n",
"\n",
"The $\\binom{n}{k}$ tells us how many ways are there to get $k$ heads our of $n$ total number of coin flips?\" and the $p^k(1-p)^{n-k}$ answers the question \"how likely is any given $k$ heads and $n-k$ tails?\", multiply them together and we get the probability of getting exactly $k$ heads.\n",
"\n",
"Now that we understand the classic method, let's use it to test whether we are actually tossing a fair coin."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:32.669687Z",
"start_time": "2021-04-01T04:02:31.389385Z"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"image/png": {
"height": 386,
"width": 522
}
},
"output_type": "display_data"
}
],
"source": [
"# Calculate the probability for every possible outcome\n",
"# of tossing a fair coin 30 k_range\n",
"\n",
"k_range = range(1, 31) # number of heads appearing\n",
"n = 30 # number of k_range tossing the coin\n",
"p = 0.5 # probability of coin appearing up as head\n",
"\n",
"prob = stats.binom(n = n, p = p).pmf(k = k_range)\n",
"\n",
"# Plot the probability distribution using the probabilities list \n",
"# we created above.\n",
"plt.step(k_range, prob, where = 'mid', color = blue)\n",
"plt.xlabel('Number of heads')\n",
"plt.ylabel('Probability')\n",
"plt.plot((22, 22), (0, 0.1599), color = red)\n",
"plt.annotate('0.8%', xytext = (25, 0.08), xy = (22, 0.08), \n",
" va = 'center', color = red, size = 'large',\n",
" arrowprops = {'arrowstyle': '<|-', 'lw': 2,\n",
" 'color': red, 'shrinkA': 10})\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The visualization above shows the probability distribution for flipping a fair coin 30 times. Using this visualization we can now determine the probability of getting, say for example, 12 heads in 30 flips, which looks to be about 8%. Notice that we've labeled our example of 22 heads as 0.8%. If we look at the probability of flipping exactly 22 heads, it looks likes to be a little less than 0.8%, in fact if we calculate it using the function from above, we get 0.5%."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:32.701525Z",
"start_time": "2021-04-01T04:02:32.672087Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Probability of flipping 22 heads: 0.5%\n"
]
}
],
"source": [
"prob = stats.binom(n = n, p = p).pmf(k = 22)\n",
"print('Probability of flipping 22 heads: {:0.1f}%'.format(prob * 100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, then why do we have 0.8% labeled in our probability distribution above? Well, that's because we are showing the probability of getting at least 22 heads, which is also known as the **p-value**.\n",
"\n",
"Let's pull back from our example and discuss formally about hypothesis testing. In standard frequentist statistic's hypothesis testing, we start with a null hypothesis that we usually call $H_0$ (pronouced as H naught), which represents our status quo. On the other hand, we also have an alternative hypothesis our $H_1$ that represents the question that we wish to answer, i.e. what we’re testing for.\n",
"\n",
"After setting up our null and alternative hypothesis, we conduct a hypothesis test under the assumption that the null hypothesis is true. If the test results suggest that the data do not provide convincing evidence for the alternative hypothesis, we stick with the null hypothesis. If they do, then we reject the null hypothesis in favor of the alternative.\n",
"\n",
"Frequentist statistic's hypothesis testing uses a p-value to weigh the strength of the evidence (what the data is telling you about the population). p-value is defined as **the probability of obtaining the observed or more extreme outcome, given that the null hypothesis is true (not the probability that the alternative hypthesis is true)**. It is a number between 0 and 1 and interpreted in the following way:\n",
"\n",
"- A small p-value (typically <= 0.05, 0.05 is a commonly used threshold, the threshold is often denoted as $\\alpha$) indicates strong evidence against the null hypothesis, so we reject the null hypothesis. This means that something interesting is going on and it’s not just noise!\n",
"- A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so we fail to reject the null hypothesis. Although p-value is still in our favor, we cannot conclusively say that it was not due to random noise.\n",
"- p-values very close to the cutoff (0.05) are considered to be marginal (could go either way). If you carefully read good papers on these kind of topics, you will always see the p-values being reported so that the readers can draw their own conclusions.\n",
"\n",
"**Example:**\n",
"\n",
"Let's say that a pizza place claims their delivery times are 30 minutes or less on average. Now we think it's actually takes more than 30 minutes. We conduct a hypothesis test because we believe the null hypothesis, that the mean delivery time is 30 minutes maximum, is incorrect. This means that our alternative hypothesis is the mean time is greater than 30 minutes. We randomly sample some delivery times and run the data through the hypothesis test, and our p-value turns out to be 0.01, which is much less than 0.05.\n",
"\n",
"In real terms, there is a probability of 0.01 that we will mistakenly reject the pizza place's claim that their delivery time is less than or equal to 30 minutes. Since typically we are willing to reject the null hypothesis when this probability is less than 0.05, we conclude that the pizza place is wrong; their delivery times are in fact more than 30 minutes on average."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Back with our coin toss example, the null hypothesis assumes we have a fair coin, and the way we determine if this hypothesis is true or not is by calculating how often flipping this fair coin 30 times would result in 22 or more heads. If we then take the number of times that we got 22 or more heads and divide that number by the total of all possible permutations of 30 coin tosses, we get the probability of tossing 22 or more heads with a fair coin. This probability is essentially our p-value."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:32.736892Z",
"start_time": "2021-04-01T04:02:32.706352Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value: 0.8%\n"
]
}
],
"source": [
"def compute_pvalue(n, k, p):\n",
" \"\"\"Returns the p-value for binomial distribution\"\"\"\n",
" k_range = range(k, n + 1)\n",
" pvalue = stats.binom(n = n, p = p).pmf(k = k_range).sum()\n",
" return pvalue\n",
"\n",
"\n",
"pvalue = compute_pvalue(n = 30, k = 22, p = 0.5)\n",
"print('P-value: {:0.1f}%'.format(pvalue * 100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The role of p-value is used to check the validity of the null hypothesis. The way this is done is by agreeing upon some predetermined upper limit for our p-value, below which we will assume that our null hypothesis is false.\n",
"\n",
"In other words, if our null hypothesis were true, and 22 heads in 30 flips could happen often enough by chance, we would expect to see it happen more often than the given threshold percentage of times. So, for example, if we chose 10% as our p-value threshold, then we would expect to see 22 or more heads show up at least 10% of the time to determine that this is a chance occurrence and not due to some bias in the coin. Historically, the generally accepted threshold has been 5%, and so if our p-value is less than 5%, we can then make the assumption that our coin may not be fair.\n",
"\n",
"Running the code above gives us a p-value of roughly 0.8%, which matches the value in our probability distribution above and is also less than the 5% threshold needed to reject our null hypothesis, so it does look like we may have a biased coin."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:32.769785Z",
"start_time": "2021-04-01T04:02:32.739966Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value: 0.8%\n"
]
}
],
"source": [
"# we can also use the binom_test function from scipy to\n",
"# perform the hypothesis testing\n",
"pvalue = stats.binom_test(x = 22, n = 30, p = 0.5, alternative = 'greater')\n",
"print('P-value: {:0.1f}%'.format(pvalue * 100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instead of using the statistical approach, the code below seeks to answer the same question of whether or not our coin is fair by running a large number of simulated coin flips and calculating the proportion of these experiments that resulted in at least 22 heads or more."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:34.993555Z",
"start_time": "2021-04-01T04:02:33.672906Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simulated P-value: 0.8%\n"
]
}
],
"source": [
"def coin_toss(n_simulation = 100000):\n",
" \"\"\"\n",
" computing a fair coin resulting in at\n",
" least 22 heads or more through simulation\n",
" \"\"\"\n",
" pvalue = 0\n",
" for i in range(n_simulation):\n",
" # trials: 1 denotes head, 0 denotes tail\n",
" trials = np.random.randint(2, size = 30)\n",
" if trials.sum() >= 22:\n",
" pvalue += 1\n",
"\n",
" pvalue /= n_simulation\n",
" return pvalue\n",
"\n",
"\n",
"pvalue = coin_toss()\n",
"print('Simulated P-value: {:0.1f}%'.format(pvalue * 100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The result of our simulations is 0.8%, the exact same result we got earlier when we calculated the p-value using the classical method above."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frequentist A/B testing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A/B testing is essentially a simple randomized trial. Randomized trials are (usually) considered the gold standard study design for evaluating the efficacy of new medical treatments, but they are also used much more widely in experimental research.\n",
"\n",
"For example, when someone visits a website, the site sends them to one of two (or possibly more) different landing or home pages, and which one they are sent to is chosen at random. The purpose is to determine which page version generates a superior outcome, e.g. which page generates more advertising revenue, or which which page leads a greater proportion of visitors to continue visiting the site.\n",
"\n",
"The key idea is that because we randomize which landing page (or treatment in the case of a randomized clinical trial) someone goes to, after a large number of visitors, the groups of people who visited the two pages are completely comparable in respect of all characteristics (e.g. age, gender, location, and anything else you can think of!). Because the two groups are comparable, we can compare the outcomes (e.g. amount of advertising revenue) between the two groups to obtain an unbiased, and fair, assessment of the relative effectiveness (in terms of our defined outcome) of the two designs.\n",
"\n",
"Suppose for the moment that we've had two visitors to our site, and one visitor has been randomized to page A, and the other visitor to page B (note that it is entirely possible, with simple randomization, that both visitors could have both been sent to page A). Suppose next that the visitor to page A generated revenue, but the visitor to page B generated no revenue. Should we conclude that page A is superior to page B, in terms of revenue generation? Of course not. Because we have only sampled two visitors, it is entirely possible that the visitor to page A would have generated revenue even if they had been sent to page B, perhaps because they are very interested in the site's content, whereas perhaps the visitor to page B was not particularly interested in the site content, and was never going to generate revenue. We can overcome this problem by running the A/B testing for a sufficiently large number of visitors, such that the probability that the scenario described above is sufficiently small.\n",
"\n",
"Scenario: We ran an A/B test with two different versions of a web page, a and b, for which we count the number of visitors and whether they convert or not. We can summarize this in a contingency table showing the frequency distribution of the events:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:35.034732Z",
"start_time": "2021-04-01T04:02:34.995591Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
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" \n",
"
\n",
"
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"
version
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not_converted
\n",
"
converted
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0
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" version not_converted converted\n",
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},
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.DataFrame({\n",
" 'version': ['A', 'B'],\n",
" 'not_converted': [4514, 4473],\n",
" 'converted': [486, 527]\n",
"})[['version', 'not_converted', 'converted']]\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is trivial to compute the conversion rate of each version, 486/(486 + 4514) = 9.72% for a and 10.5% for b. With such a relatively small difference, however, can we convincingly say that the version b converts better? To test the statistical significance of a result like this, a hypothesis testing can be used."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparing Two Proportions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's formalize our thought process a little bit, suppose that we have obtained data from n visitors, $n_A$ of which have been (randomly) sent to page A, and $n_B$ of which have been sent to page B. Further, let $X_A$ and $X_B$ denote the number of visitors for whom we obtained a 'successful' outcome in the two groups. The proportion of successes in the two groups is then given by $\\hat{p_A} = X_A/n_A$ and $\\hat{p_B} = X_B/n_B$ respectively. The estimated difference in success rates is then give by the difference in proportions: $\\hat{p_A} - \\hat{p_B}$:\n",
"\n",
"To assess whether we have statistical evidence that the two pages' success rates truely differ, we can perform a hypothesis test. The null hypothesis that we want to test is that the two pages' true success rates are equal, whereas the alternative is that they differ (one is higher than the other). If $p_A$ = the proportion of the page A population whom we obtained a successful outcome and $p_B$ = the proportion of the page B population whom we obtained a successful outcome then we are interested in testing the following hypothesis:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"H_0:p_A = p_B \\text{ versus } H_A: p_A \\neq p_B\n",
"\\end{align}\n",
"$$\n",
"\n",
"Or put it in another way, the null hypothesis says that the factors 'page type' and 'outcome' are statistically independent of each other. In words, this means knowing which page someone is sent to tells you nothing about the chance that they will have a successful outcome. Now that we know what hypothesis test we're interested in, we'll have to derive the appropriate test statistic.\n",
"\n",
"A test statistic is a single metric that can be used to evaluate the null hypothesis and the standard way to obtain this metric is to compute the z-score that measures how many standard deviations below or above the population mean a raw score is: \n",
"\n",
"$$\n",
"\\begin{align}\n",
"z = \\frac{x - \\mu}{SE}\n",
"\\end{align}\n",
"$$\n",
"\n",
"Where:\n",
"\n",
"- $\\mu$ denotes the mean\n",
"- $SE$ or sometimes seen as the symbol $\\sigma$ denotes the standard error, computed by $\\frac{s}{\\sqrt{n}}$, where $s$ denotes the standard error and $n$ denotes the number of samples\n",
"\n",
"The following link contains an example of where this is applied in proportion hypothesis testing for those who feels uncomfortable with this concept. [Notes: Eberly College of Science STAT 414/415: Test About Proportions](https://onlinecourses.science.psu.edu/stat414/node/265)\n",
"\n",
"For our test the underlying metric is a binary yes/no variable (event), which means the appropriate test statistic is a test for differences in proportions:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"Z = \\frac{ (\\hat{p_A} - \\hat{p_B}) - (p_A - p_B) }{SE(p_A - p_B)}\n",
"\\end{align}\n",
"$$\n",
"\n",
"The test statistic makes sense as it measuring the difference in the observed proportions and the estimated proportion, standardized by an estimate of the standard error of this quantity.\n",
"\n",
"To compute the test statistic, we first need to find the standard deviation/variance of $p_A - p_B$:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"Var(p_A - p_B)\n",
"&= Var(p_A) + Var(p_B) \\\\\n",
"&= \\frac{p_A (1 - p_A)}{n_A} + \\frac{p_B (1 - p_B)}{n_B} \\\\\n",
"&= p (1 - p) \\left( \\frac{1}{n_A} + \\frac{1}{n_B} \\right)\n",
"\\end{align}\n",
"$$\n",
"\n",
"- The first step stems from that fact that, given that we know:\n",
" - The variance of a random variable X is defined as $Var(X) = E[X^2] - E[X]^2$\n",
" - The covariance between two random variable X and Y is defined as $Cov(X, Y) = E[(X - u_x)(y - u_y)] = E[XY] - E[X]E[Y]$\n",
" - When conducting hypothesis test, we know that the two groups should be independent of each other, i.e. the covariance between the two should be 0\n",
"\n",
"$$\n",
"\\begin{align}\n",
"Var(X - Y)\n",
"&= E[(X - Y)(X - Y)] - E[X - Y]^2 \\\\\n",
"&= E[X^2 - 2XY + Y^2] - (u_x - u_y)^2 \\\\\n",
"&= E[X^2 - 2XY + Y^2] - u_x^2 + 2u_xu_y - u_y^2 \\\\\n",
"&= (E[X^2] - u_x^2) + (E[Y^2] - u_y^2) - 2(E[XY] - u_xu_y) \\\\\n",
"&= Var(X) + Var(Y) - 2 Cov(X, Y)\n",
"\\end{align}\n",
"$$\n",
"\n",
"- We're using the property that the variance of a binomial proportion is given by: $Var(p_A) = p_A (1 - p_A) / n_A$, the same can be applied for group B\n",
"- The third step comes from the fact that if we assume that the null hypothesis, $p_A = p_B$ is true, then the population proportions equal some common value $p$, that is, $p_A = p_B = p$. Since we don't know the assumed common population proportion $p$ any more than we know the proportions $p_A$ and $p_B$ of each population, we can estimate $p$ using the proportion of \"successes\" in the two combined, $\\hat{p} = (X_A + X_B)/(n_A + n_B)$, which is commonly referred to as the **pooled probability**\n",
"\n",
"During the third step, we utilized that fact that if we assume that the null hypothesis is true, then $p_A = p_B$, this also means $p_A - p_B = 0$. Given all of these information, the formula for our test statistic now becomes:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"Z\n",
"&= \\frac{ (\\hat{p_A} - \\hat{p_B}) - (p_A - p_B) }{SE(p_A - p_B)} \\\\\n",
"&= \\frac{ (\\hat{p_A} - \\hat{p_B}) - 0 }{\\sqrt{\\hat{p} (1 - \\hat{p}) \\left( \\frac{1}{n_A} + \\frac{1}{n_B} \\right)}}\n",
"\\end{align}\n",
"$$\n",
"\n",
"Where $\\hat{p} = (X_A + X_B)/(n_A + n_B)$"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:36.680744Z",
"start_time": "2021-04-01T04:02:36.630495Z"
}
},
"outputs": [],
"source": [
"def two_proprotions_test(success_a, size_a, success_b, size_b):\n",
" \"\"\"\n",
" A/B test for two proportions;\n",
" given a success a trial size of group A and B compute\n",
" its zscore and pvalue\n",
" \n",
" Parameters\n",
" ----------\n",
" success_a, success_b : int\n",
" Number of successes in each group\n",
" \n",
" size_a, size_b : int\n",
" Size, or number of observations in each group\n",
" \n",
" Returns\n",
" -------\n",
" zscore : float\n",
" test statistic for the two proportion z-test\n",
"\n",
" pvalue : float\n",
" p-value for the two proportion z-test\n",
" \"\"\"\n",
" prop_a = success_a / size_a\n",
" prop_b = success_b / size_b\n",
" prop_pooled = (success_a + success_b) / (size_a + size_b)\n",
" var = prop_pooled * (1 - prop_pooled) * (1 / size_a + 1 / size_b)\n",
" zscore = np.abs(prop_b - prop_a) / np.sqrt(var)\n",
" one_side = 1 - stats.norm(loc = 0, scale = 1).cdf(zscore)\n",
" pvalue = one_side * 2\n",
" return zscore, pvalue"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:36.728668Z",
"start_time": "2021-04-01T04:02:36.684042Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"zscore = 1.359, pvalue = 0.174\n"
]
}
],
"source": [
"success_a = 486\n",
"size_a = 5000\n",
"success_b = 527\n",
"size_b = 5000\n",
"\n",
"zscore, pvalue = two_proprotions_test(success_a, size_a, success_b, size_b)\n",
"print('zscore = {:.3f}, pvalue = {:.3f}'.format(zscore, pvalue))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:36.771986Z",
"start_time": "2021-04-01T04:02:36.733898Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"zscore = -1.359, pvalue = 0.174\n"
]
}
],
"source": [
"# or we can use the implementation from statsmodels\n",
"# where we pass in the success (they call the argument counts)\n",
"# and the total number for each group (they call the argument nobs,\n",
"# number of observations)\n",
"counts = np.array([486, 527])\n",
"nobs = np.array([5000, 5000])\n",
"\n",
"zscore, pvalue = proportions_ztest(counts, nobs, alternative = 'two-sided')\n",
"print('zscore = {:.3f}, pvalue = {:.3f}'.format(zscore, pvalue))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based on the fact that our p-value is not smaller than the 0.05 commonly used threshold, the test statistic tells us we do not have strong evidence against our null hypothesis, i.e. we do not have strong evidence that the two pages are not equally effective.\n",
"\n",
"Apart from spitting out the p-value, we will also look at forming a confidence interval for $\\hat{p_A} - \\hat{p_B}$. If the number of trials in both groups is large, and the observed number of successes are not too small, we can calculate a 95% confidence interval using the formula:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"\\text{point estimate} \\pm z * SE\n",
"&= (\\hat{p_A} - \\hat{p_B}) \\pm z * \\frac{p_A (1 - p_A)}{n_A} + \\frac{p_B (1 - p_B)}{n_B}\n",
"\\end{align}\n",
"$$\n",
"\n",
"Note that when calculating the confidence interval because we no longer have the assumption that $p_A = p_B$ from our null hypothesis, thus we can't leverage this property and use the pooled probability."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:37.206427Z",
"start_time": "2021-04-01T04:02:37.164114Z"
}
},
"outputs": [],
"source": [
"def two_proprotions_confint(success_a, size_a, success_b, size_b, significance = 0.05):\n",
" \"\"\"\n",
" A/B test for two proportions;\n",
" given a success a trial size of group A and B compute\n",
" its confidence interval;\n",
" resulting confidence interval matches R's prop.test function\n",
" \n",
" Parameters\n",
" ----------\n",
" success_a, success_b : int\n",
" Number of successes in each group\n",
" \n",
" size_a, size_b : int\n",
" Size, or number of observations in each group\n",
" \n",
" significance : float, default 0.05\n",
" Often denoted as alpha. Governs the chance of a false positive.\n",
" A significance level of 0.05 means that there is a 5% chance of\n",
" a false positive. In other words, our confidence level is\n",
" 1 - 0.05 = 0.95\n",
" \n",
" Returns\n",
" -------\n",
" prop_diff : float\n",
" Difference between the two proportion\n",
" \n",
" confint : 1d ndarray\n",
" Confidence interval of the two proportion test\n",
" \"\"\"\n",
" prop_a = success_a / size_a\n",
" prop_b = success_b / size_b\n",
" var = prop_a * (1 - prop_a) / size_a + prop_b * (1 - prop_b) / size_b\n",
" se = np.sqrt(var)\n",
" \n",
" # z critical value\n",
" confidence = 1 - significance\n",
" z = stats.norm(loc = 0, scale = 1).ppf(confidence + significance / 2)\n",
"\n",
" # standard formula for the confidence interval\n",
" # point-estimtate +- z * standard-error\n",
" prop_diff = prop_b - prop_a\n",
" confint = prop_diff + np.array([-1, 1]) * z * se\n",
" return prop_diff, confint"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:37.239463Z",
"start_time": "2021-04-01T04:02:37.209145Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"estimate difference: 0.008199999999999999\n",
"confidence interval: [-0.00362633 0.02002633]\n"
]
}
],
"source": [
"prop_diff, confint = two_proprotions_confint(success_a, size_a, success_b, size_b)\n",
"print('estimate difference:', prop_diff)\n",
"print('confidence interval:', confint)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Up till this point, we've been using the 5000 as the total number of observations/samples that are involved in the A/B testing process. The next question that we'll address is, in real world scenarios, how many obeservations do we need in order to draw a valid verdict on the test result. This leads us to our next topic **power**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introducing Power"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the world of hypothesis testing, rejecting the null hypothesis when it is actually true is called a type 1 error, often denoted as $\\alpha$. Committing a type 1 error is a false positive because we end up recommending something that does not work. Conversely, a type 2 error, often denoted as $\\beta$, occurs when we do not reject the null hypothesis when it is actually false. This is a false negative because we end up sitting on our hands when we should have taken action. We need to consider both types of errors when choosing the sample size.\n",
"\n",
"Two important probabilities related to type 1 and type 2 error are:\n",
"\n",
"- **Significance level:** Governs the chance of a false positive. A significance level of 0.05 means that there is a 5% chance of a false positive. Choosing level of significance is an arbitrary task, but for many applications, a level of 5% is chosen, for no better reason than that it is conventional\n",
"- **Statistical power** Power of 0.80 means that there is an 80% chance that if there was an effect, we would detect it (or a 20% chance that we'd miss the effect). In other words, power is equivalent to $1 - \\beta$. There are no formal standards for power, most researchers assess the power of their tests using 0.80 for adequacy\n",
"\n",
"| Scenario | $H_0$ is true | $H_0$ is false |\n",
"|:--------------:|:----------------------------------:|:-------------------------:|\n",
"| Accept $H_0$ | Correct Decision | Type 2 Error (1 - power) |\n",
"| Reject $H_0$ | Type 1 Error (significance level) | Correct decision |\n",
"\n",
"The concepts of power and significance level can seem somewhat convoluted at first glance. A good way to get a feel for the underlying mechanics is to plot the probability distribution of $Z$ assuming that the null hypothesis is true. Then do the same assuming that the alternative hypothesis is true, and overlay the two plots.\n",
"\n",
"Consider the following example: $H_0: p_A = p_B, H_1: p_A > p_B$. A one-sided test was chosen here for charting-simplicity.\n",
"\n",
"- Total sample size, N=5,000 (assume equal sample sizes for the control and experiment groups, meaning exactly 2,500 in each group)\n",
"- Say we've decided we need to observe a difference of 0.02 in order to for us to be satisfied the intervention worked (i.e., assuming that our original baseline, $p_B$ was 0.08, then we want $p_A = 0.10$). We will discuss how to make this decision later in the post"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:38.844552Z",
"start_time": "2021-04-01T04:02:38.804003Z"
}
},
"outputs": [],
"source": [
"def plot_power(min_diff, prob_b, size_a, size_b, significance = 0.05):\n",
" \"\"\"illustrating power through a one-tailed hypothesis test\"\"\"\n",
" \n",
" # obtain the z-score for the minimum detectable\n",
" # difference using proportion_ztest\n",
" prob_a = prob_b + min_diff\n",
" count_a = size_a * prob_a\n",
" count_b = size_b * prob_b\n",
" counts = np.array([count_a, count_b])\n",
" nobs = np.array([size_a, size_b])\n",
" zscore, _ = proportions_ztest(counts, nobs, alternative = 'larger')\n",
"\n",
" # distribution for the null hypothesis, h0\n",
" # and alternative hypothesis, h1\n",
" h0 = stats.norm(loc = 0, scale = 1)\n",
" h1 = stats.norm(loc = zscore, scale = 1)\n",
"\n",
" # points that are greater than the zscore for the\n",
" # specified significance level\n",
" x = np.linspace(-5, 6, num = 100)\n",
" threshold = h0.ppf(1 - significance)\n",
" mask = x > threshold\n",
" \n",
" # power is the area after the threshold, i.e.\n",
" # 1 - the cumulative distribution function of that point\n",
" power = np.round(1 - h1.cdf(threshold), 2)\n",
"\n",
" hypotheses = [h1, h0]\n",
" labels = ['$H_1$ is true', '$H_0$ is true']\n",
" for hypothesis, label in zip(hypotheses, labels):\n",
" y = hypothesis.pdf(x)\n",
" line = plt.plot(x, y, label = label) \n",
" plt.fill_between(x = x[mask], y1 = 0.0, y2 = y[mask],\n",
" alpha = 0.2, color = line[0].get_color())\n",
" \n",
" title = 'p1: {}, p2: {}, size1: {}, size2: {}, power: {}'\n",
" plt.title(title.format(prob_a, prob_b, size_a, size_b, power))\n",
" plt.legend()\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:40.401828Z",
"start_time": "2021-04-01T04:02:38.846922Z"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"image/png": {
"height": 407,
"width": 551
}
},
"output_type": "display_data"
}
],
"source": [
"prob_b = 0.08\n",
"min_diff = 0.02\n",
"size_a = 2500\n",
"size_b = 2500\n",
"\n",
"plot_power(min_diff, prob_b, size_a, size_b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The shaded green area denotes the significance region, while the shaded blue area denotes the power (note that it includes the shaded green area). Note that if we pick a smaller N, or a smaller probability difference between the control and experiment group, the power drops (the shaded blue area decreases), meaning that if there’s is in fact a change, there’s lesser percent chance that we’ll detect it."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:41.348688Z",
"start_time": "2021-04-01T04:02:40.406616Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"image/png": {
"height": 407,
"width": 545
}
},
"output_type": "display_data"
}
],
"source": [
"# smaller probability difference\n",
"prob_b = 0.08\n",
"min_diff = 0.001\n",
"size_a = 2500\n",
"size_b = 2500\n",
"\n",
"plot_power(min_diff, prob_b, size_a, size_b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following link illustrates power for a two-sided hypothesis test for those interested. [Youtube: Calculating Power and the Probability of a Type II Error (A Two-Tailed Example)](https://www.youtube.com/watch?v=NbeHZp23ubs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Determining Sample Size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Say we've followed the rule of thumb and required the significance level to be 5% and the power to be 80%. This means we have now specified the two key components of a power analysis.\n",
"\n",
"- A decision rule of when to reject the null hypothesis. We reject the null when the p-value is less than 5%.\n",
"- Our tolerance for committing type 2 error (1−80%=20%).\n",
"\n",
"To actually solve for the equation of finding the suitable sample size, we also need to specify the detectable difference, i.e. the level of impact we want to be able to detect with our test.\n",
" \n",
"In order to explain the dynamics behind this, we'll return to the definition of power: the power is the probability of rejecting the null hypothesis when it is false. Hence for us to calculate the power, we need to define what \"false\" means to us in the context of the study. In other words, how much impact, i.e., difference between test and control, do we need to observe in order to reject the null hypothesis and conclude that the action worked?\n",
"\n",
"Let's consider two illustrative examples: if we think that an event rate reduction of, say, $10^{-10}$ is enough to reject the null hypothesis, then we need a very large sample size to get a power of 80%. This is pretty easy to deduce from the charts above: if the difference in event rates between test and control is a small number like $10^{-10}$, the null and alternative probability distributions will be nearly indistinguishable. Hence we will need to increase the sample size in order to move the alternative distribution to the right and gain power. Conversely, if we only require a reduction of 0.02 in order to claim success, we can make do with a much smaller sample size. \n",
"\n",
"> The smaller the detectable difference, the larger the required sample size\n",
"\n",
"Here's how we could conduct a power test in python:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:42.261292Z",
"start_time": "2021-04-01T04:02:42.141073Z"
}
},
"outputs": [],
"source": [
"import statsmodels.stats.api as sms\n",
"\n",
"\n",
"def compute_sample_size(prop1, min_diff, significance = 0.05, power = 0.8):\n",
" \"\"\"\n",
" Computes the sample sized required for a two-proportion A/B test;\n",
" result matches R's pwr.2p.test from the pwr package\n",
" \n",
" Parameters\n",
" ----------\n",
" prop1 : float\n",
" The baseline proportion, e.g. conversion rate \n",
" \n",
" min_diff : float\n",
" Minimum detectable difference\n",
" \n",
" significance : float, default 0.05\n",
" Often denoted as alpha. Governs the chance of a false positive.\n",
" A significance level of 0.05 means that there is a 5% chance of\n",
" a false positive. In other words, our confidence level is\n",
" 1 - 0.05 = 0.95\n",
" \n",
" power : float, default 0.8\n",
" Often denoted as beta. Power of 0.80 means that there is an 80%\n",
" chance that if there was an effect, we would detect it\n",
" (or a 20% chance that we'd miss the effect)\n",
" \n",
" Returns\n",
" -------\n",
" sample_size : int\n",
" Required sample size for each group of the experiment\n",
"\n",
" References\n",
" ----------\n",
" R pwr package's vignette\n",
" - https://cran.r-project.org/web/packages/pwr/vignettes/pwr-vignette.html\n",
"\n",
" Stackoverflow: Is there a python (scipy) function to determine parameters\n",
" needed to obtain a target power?\n",
" - https://stackoverflow.com/questions/15204070/is-there-a-python-scipy-function-to-determine-parameters-needed-to-obtain-a-ta\n",
" \"\"\"\n",
" prop2 = prop1 + min_diff\n",
" effect_size = sms.proportion_effectsize(prop1, prop2)\n",
" sample_size = sms.NormalIndPower().solve_power(\n",
" effect_size, power = power, alpha = significance, ratio = 1)\n",
" \n",
" return sample_size"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:42.297847Z",
"start_time": "2021-04-01T04:02:42.263356Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sample size required per group: 3834.5957398840183\n"
]
}
],
"source": [
"sample_size = compute_sample_size(prop1 = 0.1, min_diff = 0.02)\n",
"print('sample size required per group:', sample_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the printed result is the sample size needed for each group!\n",
"\n",
"Unlike the significance level and the power, there are no plug-and-play values we can use for the detectable difference. The key is to define what \"pay off\" means for the study at hand, which depends on what the adverse event is a well as the cost of the action. Two guiding principles:\n",
"\n",
"- **Avoid wasteful sampling** Let’s say it takes an absolute difference of 0.02 between test and control in order for the treatment to pay off. In this case, aiming for a 0.01 detectable difference would just lead to more precision than we really need. Why have the ability to detect 0.01 if we don’t really care about a 0.01 difference? In many cases, sampling for unnecessary precision can be costly and a waste of time\n",
"- **Avoid missed opportunities** Conversely, if we are analyzing a sensitive metric where small changes can have a large impact e.g. email campaigns, we have to aim for a small detectable difference. If we choose an insufficient sample size, we may end up sitting on our hands and missing an opportunity (type 2 error)\n",
"\n",
"Hence, choosing the minimum detectable difference should be a cross-functional analysis/discussion between the data scientist and the business stakeholder. Once there is a viable range for the detectable difference, we can evaluate the sample size required for each option. For example, let’s say that $p1=0.10$ and we want the detectable difference to be between 0.01 and 0.03. Clearly, we’d rather be able to detect a difference of 0.01, but it may be too costly and hence we want to evaluate more conservative options as well."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:43.160079Z",
"start_time": "2021-04-01T04:02:42.300059Z"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"image/png": {
"height": 405,
"width": 554
}
},
"output_type": "display_data"
}
],
"source": [
"# calculate the the required sample size\n",
"# for a range of minimum detectable difference\n",
"sample_sizes = []\n",
"min_diffs = np.arange(0.01, 0.03, 0.001)\n",
"for min_diff in min_diffs:\n",
" sample_size = compute_sample_size(prop1 = 0.1, min_diff = min_diff)\n",
" sample_sizes.append(sample_size)\n",
"\n",
"plt.plot(min_diffs, sample_sizes)\n",
"plt.title('Sample Size Required for the Minimum Detectable Difference')\n",
"plt.ylabel('Sample Size')\n",
"plt.xlabel('Minimum Detectable Difference')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the graph, we can see that we need roughly 10x more observations to get a detectable difference of 0.01 compared to 0.03.\n",
"\n",
"The following section is an alternative way of conducting a test statistic for proportional A/B test, feel free to skip it as it will not affect the understanding of later section."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Alternative View of the Test Statistic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are two types of the chi-squared test, goodness of fit and test of independence, where the latter is more applicable for our use case here. We start off by converting the contingency table into a probability matrix by dividing each element with the total frequencies:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:43.238733Z",
"start_time": "2021-04-01T04:02:43.163072Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
version
\n",
"
not_converted
\n",
"
converted
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
A
\n",
"
0.4514
\n",
"
0.0486
\n",
"
\n",
"
\n",
"
1
\n",
"
B
\n",
"
0.4473
\n",
"
0.0527
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" version not_converted converted\n",
"0 A 0.4514 0.0486\n",
"1 B 0.4473 0.0527"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols = ['not_converted', 'converted']\n",
"data[cols] = data[cols] / data[cols].values.sum()\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will denote $V$ as the version of the web page ($a$ or $b$) and $C$ as the conversion result, $f$ (false did not convert) or $t$ (true did in fact convert). The table that we computed above, which this the data that we observed can then be translated into this form:\n",
"\n",
"\n",
"| Version (V) | $f$ (false did not convert) | $t$ (true did in fact convert) |\n",
"|:-----------:|:----------------------------:|:------------------------------:|\n",
"| A | $P(V = a, C = f)$ | $P(V = a, C = t)$ |\n",
"| B | $P(V = b, C = f)$ | $P(V = b, C = t)$ |\n",
"\n",
"\n",
"Now, our interest is whether the conversion $C$ depends on the page version $V$, and if it does, to learn which version converts better. In probability theory, the events $C$ and $V$ are said to be independent if the joint probability can be computed by $P(V, C) = P(V) \\cdot P(C)$, where $P(V)$ and $P(C)$ are marginal probabilities of $V$ and $C$, respectively. It is straightforward to compute the marginal probabilities from row and column marginals:\n",
"\n",
"$$P(V = a) = \\frac{4514 + 486}{10000} \\hspace{1cm} P(V = b) = \\frac{4473 + 527}{10000}$$\n",
"$$P(C = f) = \\frac{4514 + 4473}{10000} \\hspace{1cm} P(C = t) = \\frac{486 + 527}{10000}$$\n",
"\n",
"Our null hypothesis is $V$ and $C$ are independent, in which case the elements of the matrix, a.k.a the distribution that we're expecting is equivalent to:\n",
"\n",
"| Version (V) | $f$ (false did not convert) | $t$ (true did in fact convert) |\n",
"|:-----------:|:----------------------------:|:------------------------------:|\n",
"| A | $P(V = a)P(C = f)$ | $P(V = a)P(C = t)$ |\n",
"| B | $P(V = b)P(C = f)$ | $P(V = b)P(C = t)$ |\n",
"\n",
"\n",
"The conversion $C$ is said to be dependent on the version $V$ of the web site if this null hypothesis is rejected. Hence rejecting the null hypothesis means that one version is better at converting than the other.\n",
"\n",
"When dealing with counts and investigating how far the observed counts are from the expected counts, we use a test statistic called the **chi-square test**. The chi-squared test compares an observed distribution $O_{ij}$ to an expected distribution $E_{ij}$:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"\\chi^2 = \\sum_{i,j} \\frac{(O_{ij} - E_{ij})^2}{E_{ij}}\n",
"\\end{align}\n",
"$$\n",
"\n",
"It's calculated as the observed minus the expected for each cell squared divided by the expected counts, the division with the expected counts makes final result proportional to our expected frequency. After performing the computation for each cell, we want to sum this over all the cells (levels of the categorical variable).\n",
"\n",
"This $\\chi^2$ probability distribution has only one parameter, the degrees of freedom. It influences the shape, the center and the spread of the chi-square distribution."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:44.109803Z",
"start_time": "2021-04-01T04:02:43.241511Z"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 414,
"width": 523
}
},
"output_type": "display_data"
}
],
"source": [
"# chi square distribution with varying degrees of freedom\n",
"fig = plt.figure(figsize = (8, 6))\n",
"x = np.linspace(0, 5, 1000)\n",
"deg_of_freedom = [1, 2, 3, 4]\n",
"for df in deg_of_freedom:\n",
" plt.plot(x, stats.chi2.pdf(x, df), label = '$df={}$'.format(df))\n",
"\n",
"plt.xlim(0, 5)\n",
"plt.ylim(0, 0.5)\n",
"plt.xlabel('$\\chi^2$')\n",
"plt.ylabel('$f(\\chi^2)$')\n",
"plt.title('$\\chi^2\\ \\mathrm{Distribution}$')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"chi-square distribution gives a way of measuring the difference between the frequencies we observe and the frequencies we expect. The smaller the value of $\\chi^2$, the smaller the difference overall between the observed and expected frequencies. The way to compute the degree of freedom for the test of independence using a $r \\times c$ contingency matrix is:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"df = (r - 1)(c - 1)\n",
"\\end{align}\n",
"$$\n",
"\n",
"Where $r$ denotes the number of rows and $c$ denotes the number of columns. The rationale behind this calculation is because degrees of freedom is the number of expected frequencies we have to calculate independently after taking into account any restrictions. The restrictions come from the row and column sum constraints, but decreased by one because the last entry in the table/matrix is determined by either the row or column sum on that row/column.\n",
"\n",
"Fortunately it is very straightforward to carry out this hypothesis testing using packages. All we need is to supply the function with a contingency matrix and it will return the $\\chi^2$ statistic and the corresponding p-value:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:44.168531Z",
"start_time": "2021-04-01T04:02:44.113278Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"chisq = 1.8464754013996965, pvalue = 0.17419388311716985\n"
]
}
],
"source": [
"# we can use the proportions_chisquare function,\n",
"# where we pass in the number of successes and\n",
"# the total number of trials/observation\n",
"count = np.array([486, 527])\n",
"nobs = np.array([5000, 5000])\n",
"\n",
"# note that in this case (a two sample case with two sided\n",
"# alternative), the test produces the same value as porportions_ztest\n",
"# since the chi-square distribution is the square of a normal distribution\n",
"chisq, pvalue, table = proportions_chisquare(count, nobs)\n",
"print('chisq = {}, pvalue = {}'.format(chisq, pvalue))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:44.259097Z",
"start_time": "2021-04-01T04:02:44.173450Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"chisq = 1.8464754013996965, pvalue = 0.17419388311716985\n"
]
}
],
"source": [
"# or the chi2_contingency function where we pass\n",
"# in the observed contingency table\n",
"observed = np.array([[4514, 486], [4473, 527]])\n",
"\n",
"# more about the correction = False parameter later\n",
"result = stats.chi2_contingency(observed, correction = False)\n",
"chisq, pvalue = result[:2]\n",
"print('chisq = {}, pvalue = {}'.format(chisq, pvalue))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The result for our experiment has a $\\chi^2 = 1.74$ and $p = 0.185$. Since the p-value is greater than the standard threshold 0.05, we cannot reject the null hypothesis that the page version and the conversion is independent. Therefore the difference in the conversion rates is not statistically significant.\n",
"\n",
"For a 2 x 2 contingency table, Yate’s chi-squared test is commonly used. This applies a correction of the form:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"\\chi^2_{Yate's} = \\sum_{i,j} \\frac{(\\big|O_{ij} - E_{ij}\\big| - 0.5)^2}{E_{ij}}\n",
"\\end{align}\n",
"$$\n",
"\n",
"to account for an error between the observed discrete distribution and the continuous chi-squared distribution (the step of -0.5 is often referred to as continuity correction)."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:44.341279Z",
"start_time": "2021-04-01T04:02:44.262212Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"chisq = 1.7575018692680038, pvalue = 0.18493641552090323\n"
]
}
],
"source": [
"# we can use the correcction form, by specifying\n",
"# correction = True\n",
"result = stats.chi2_contingency(observed, correction = True)\n",
"chisq, pvalue = result[:2]\n",
"print('chisq = {}, pvalue = {}'.format(chisq, pvalue))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, our pvalue is greater than the critical value, hence we simply would not reject the null hypothesis (that there is no relationship between the categorical variables).\n",
"\n",
"> Side note: in practice, we want to make sure that each particular scenario or cell has at least five expected counts before employing the chi-square test."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frequentist A/B Testing Workflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After diving into the technical details of conducting a frequentist A/B testing, we will now introduce one possible template/workflow/thought-process for conducting A/B testing."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Formulate Business Goals & Hypothesis Test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Define Business Goal**\n",
"\n",
"Every project or plan or test always starts with a goal e.g. A business objective for an online flower store is to \"Increase our sales by receiving online orders for our bouquets\"\n",
"\n",
"**Formulate A/B Test**\n",
"\n",
"The crux of A/B testing can be summarized into one sentence:\n",
"\n",
"> If **[Variable]**, then **[Result]**, because **[Rationale]**\n",
"\n",
"- **[Variable]** is the element such as call to action, media that we've modified\n",
"- **[Result]** is basically what we expect to see, such as more clicks, more sign-ups. The effect size of [Result] will be determined by the data\n",
"- **[Rationale]** what assumptions will be proven right/wrong after the experiment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We start by asking ourselves, what result are we expecting out of this test? To do this, we need to:\n",
"\n",
"- **Define our Key Performance Indicators.** e.g. Our flower store's business objective is to sell bouquets. Our KPI could be number of bouquets sold online.\n",
"- **Define our target metrics.** e.g. For our imaginary flower store, we can define a monthly target of 175 bouquets sold."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rationale"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A lot of times, people have the idea that A/B testing is panacea, too many people think they'll just guess their way to great conversion and revenue, when truly successful tests are typically much more complicated than that.\n",
"\n",
"After defining the high level goal and knowing the result that we're aiming for, find out (not guess) which parts of our business are underperforming or trending and why. Ways to perform this step are:\n",
"\n",
"**Quantitative methods** We can start by looking at quantitative data if we have any. These methods do a much better job answering how many and how much types of questions.\n",
"\n",
"Say we're a website, we can take a look at our conversion funnel and examine the flow from the persuasive end (top of the funnel) and the transactional end (bottom of the funnel). e.g. We can identify problems by starting from the top 5 highest bounce rate pages. During the examination, segment to spot underlying underperformance or trends.\n",
"\n",
"- **Segment by source:** Separate people who arrive on your website from e-mail campaigns, google, twitter, youtube, etc. Find answers to questions like: Is there a difference between bounce rates for those segments? Is there a difference in Visitor Loyalty between those who came from Youtube versus those who came from Twitter? What products do people who come from Youtube care about more than people who come from Google?\n",
"- **Segment by behavior:** Focus on groups of people who have similar behaviors For example, we can separate out people who visit more than ten times a month versus those that visit only twice. Do these people look for products in different price ranges? Are they from different regions? Or separate people out by the products they purchase, by order size, by people who have signed up.\n",
"\n",
"e.g. We're looking at our metric of total active users over time and we see a spike in one of the timelines. After confirming that this is not caused by seasonal variation, we can look at different segment of our visitors to see if one of the segment is causing the spike. Suppose we have chosen segment to be geographic, it might just happen that we’ve identify a large proportion of the traffic is generated by a specific region\n",
"\n",
"During the process we should ask ourselves: 1) Why is it happening? 2) How can we spread the success of other areas of the site. And it might be best for us to use qualitative methods to dig deeper and understand why, i.e. the rationale that behind the hypothesis test.\n",
"\n",
"**Qualitative methods:** Ideas for gathering qualitative data to understand the why a problem exists and how to potentially fix it:\n",
"\n",
"- Add an exit survey on our site, asking why our visitors did/didn't complete the goal\n",
"- Track what customers are saying in social media and on review sites\n",
"- User Experience Group (this is the preferred way as it is going really deep with a few users and ask qualitative questions such as what's holding them back from doing what we hope they'll do, e.g. converting)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Variable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Upon identifying the overall business goal and the possible issue, it's time to determine the variable, which is the element that we'll be testing for. e.g. we've identified through quantitative method that less than one percent of visitors sign up for our newsletter and after conducting qualitative studies it's because the call to action wording does not resonate with the audience, then our variable will be changing the call to action's wording.\n",
"\n",
"Note that we may have multiple ideas for our variable, in that case we can collate all the ideas, prioritize them based on three simple metrics:\n",
"\n",
"- **Potential** How much potential for a conversion rate increase? We can check to see if this kind of idea worked before.\n",
"- **Importance** How many visitors will be impacted from the test?\n",
"- **Ease** How easy is it to implement the test? Go for the low-hanging fruit first. \n",
"\n",
"Every test that's developed should documented so that we can review and prioritize ideas that are inspired by winning tests. Some ideas worth experimenting are: Headlines, CTA (call to actions), check-out pages, forms and the elements include:\n",
"\n",
"- Wording. e.g. Call to action or value proposition.\n",
"- Image. e.g. Replacing a general logistics image with the image of an actual employee.\n",
"- Layout. e.g. Increased the size of the contact form or amount of content on the page.\n",
"\n",
"---\n",
"\n",
"So given all of that a strong A/B test hypothesis may be:\n",
"\n",
"- If the call to action text is changed to \"Complete My Order\", the conversion rates in the checkout will increase, because the copy is more specific and personalized\n",
"- If the navigation link is removed from checkout pages, the conversation rate will increase because our website analytics shows portions of our traffic drop out of the funnel by clicking on those links"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quantitative A/B testing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Suppose we're running an educational platform and your A/B testing hypothesis is : Will changing the \"Start Now\" button from orange to pink increase how many students explore the platform's courses. So in this case the metric that's use to evaluate the change's performance is the click through probability (unique visitors who click the button / unique visitors to page). Note that it is often times impractical to use metrices such as total number of students that completed the course as it often takes weeks or months before a student can do that.\n",
"\n",
"Next we will jot down the hypothesis that we wish to test out, in our case the null and alternative hypothesis would be:\n",
"\n",
"- $H_0$: The experimental and control groups have the same probability of clicking the button. Or equivalent to saying that the differences of the two groups' probability is 0\n",
"- $H_1$: The two groups have different probability of completing a clicking the button"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define the Size and Duration"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After we've defined our hypothesis, the first question that comes into mind is how many tests do we need to run, or in a sense how long should the test last in order for us to make our decisions. To do that we can use a power analysis for two independent samples:\n",
"\n",
"Now suppose that our current baseline is 0.1, i.e. there's a 10 percent chance that people who saw the button will click it and we wish to detect a change of 2 percent in the click through rate (This change is quite high for online experiment)."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:48.836822Z",
"start_time": "2021-04-01T04:02:48.801468Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sample size required per group: 3834.5957398840183\n"
]
}
],
"source": [
"sample_size = compute_sample_size(prop1 = 0.1, min_diff = 0.02)\n",
"print('sample size required per group:', sample_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The result shows that we need at least 3835 sample size for each scenario to detect if there will actually be a 2 percent more than baseline click through probability. Note that this is only telling us the minimum sample size required per group, we still need to decide when do we want to run the experiment and for how long.\n",
"\n",
"e.g. Suppose we’ve chosen the goal to increase click-through rates, which is defined by the unique number of people who click the button versus the number of users who visited the page that the button was located. But to actually use the definition, we’ll also have to address some other questions. Such as, if the same user visits the page once and comes back a week or two later, do we still only want to count that once? Thus we’ll also need to specify a time period\n",
"\n",
"To account for this, if 99% of our visitors convert after 1 week, then we should do the following.\n",
"\n",
"- Run our test for two weeks\n",
"- Include in the test only users who show up in the first week. If a user shows up on day 13, we have not given them enough time to convert (click-through)\n",
"- At the end of the test, if a user who showed up on day 2 converts more than 7 days after he/she first arrived, he must be counted as a non-conversion\n",
"\n",
"There will be more discussion about this in the A/B Test Caveats & Advice section.\n",
"\n",
"For this step, there is also an online calculator that non-technical audience could use. [Online Calculator: Sample Size Calculator](http://www.evanmiller.org/ab-testing/sample-size.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define the Population"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another consideration is what fraction of the traffic are we going to send through the experiment. The key is to identify which population of our users will be affected by our experiment, we might want to target our experiment to that traffic (e.g. changing features specific to one language’s users) so that the rest of the population won’t dilute the effect.\n",
"\n",
"Next, depending on the problem we're looking at, we might want to use a cohort instead of a population. A cohort makes much more sense than looking at the entire population when testing out learning effects, examining user retention or anything else that requires the users to be established for some reason.\n",
"\n",
"A quick note on cohort. The gist of cohort analysis is basically putting our customers into buckets so we can track their behaviours over a period of time. The term cohort stands for a group of customers grouped by the timeline (can be week, month) where they first made a purchase (can be a different action that’s valuable to the business). Having similar traits makes the two groups more comparable.\n",
"\n",
"e.g. You’re an educational platform has an existing course that’s already up and running. Some of the students have completed the course, some of them are midway through and there’re students who have not yet started. If you want to change the structure of of one of the lessons to see if it improves the completion rate of the entire course and they started the experiment at time X. For students who have started before the experiment initiated they may have already finished the lesson already leading to the fact that they may not even see the change. So taking the whole population of students and running the experiment on them isn’t what you want. Instead, you want to segment out the cohort, the group of customers, that started the lesson as the experiment was launched and split that into an experiment and control group."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluating Result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Suppose we have ran the test and obtained the total number of sample sizes and the total number of successes for both groups. Given these variables we can use it to calculate whether the proportional change was due to variation or not."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:50.240147Z",
"start_time": "2021-04-01T04:02:50.209859Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"estimate difference: 0.03727084197203194\n",
"confidence interval: [0.02279256 0.05174912]\n"
]
}
],
"source": [
"# made-up results\n",
"success_a = 386\n",
"size_a = 3834\n",
"success_b = 530\n",
"size_b = 3842\n",
"\n",
"prob_diff, confint = two_proprotions_confint(success_a, size_a, success_b, size_b)\n",
"print('estimate difference:', prob_diff)\n",
"print('confidence interval:', confint)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to launch a change, the change should be larger than the minimum detectable change that we wished to detect. In our case, the value we've set was 0.02. Base on the result above, we can denote that since even the lower bound of the confidence interval is larger than the value, we'll definitely launch the newer version of the click button.\n",
"\n",
"There is also an online calculator that we can use to perform the proportion test. [Online Calculator: AB Testguide](https://abtestguide.com/calc/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sanity Check"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When running experiments, especially online experiments, it's a good idea to check whether the experiments were setup properly, i.e. are the users being split equally amongst the two groups. For instance, after running your experiment for a week, you've discovered that the total number of users assigned to the control group is 64454 and the total number of users assigned to the experiment group 61818. How would you figure out whether the difference is within expectation given that each user is randomly assigned to the control or experiment group with a probability of 0.5? It's usually good idea to check this.\n",
"\n",
"This is equivalent to saying out of a total 126272 (64454 + 61818) users, is it surprising to see if 64454 users are assigned to the control group? This is essentially a binomial distribution, thus, knowing this information, we can construct a confidence interval to test if the number lies within the confidence interval. The confidence interval can be calculated by the mean plus and minus the z-score times the standard error. \n",
"\n",
"\n",
"\\begin{align}\n",
"mean \\pm Z * \\sqrt{np(1 - p)}\n",
"\\end{align}\n",
"\n",
"Where the mean is expected number of users in the control / experiment group, which is simply the total number of the two groups times 0.5, since the probability of a user falling into either group is 50%. And the standard error of a binomial distribution is $\\sqrt{np(1-p)}$."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:51.102452Z",
"start_time": "2021-04-01T04:02:51.076255Z"
}
},
"outputs": [],
"source": [
"def sanity_check(size1, size2, significance = 0.05):\n",
" n = size1 + size2\n",
" confidence = 1 - significance\n",
" z = stats.norm.ppf(confidence + significance / 2)\n",
" confint = n * 0.5 + np.array([-1, 1]) * z * np.sqrt(n * 0.5 * 0.5)\n",
" return confint"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:51.131641Z",
"start_time": "2021-04-01T04:02:51.104673Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([62787.76563631, 63484.23436369])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"size1 = 64454\n",
"size2 = 61818\n",
"sanity_check(size1, size2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The result shows that 64454 does not lie within the range of the computed 95 percent confidence interval and therefore it indicates the two groups may not be split equally. \n",
"\n",
"When this kind of situation happens it's usually best to go back to the day by day data to get a better idea of what could be going wrong. One good thing is to check whether any particular day stands out, or it is just an overall pattern. If it is an overall pattern, then it is suggested that we should check if something went wrong with the experiment setup before proceeding on to analyzing the result."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A/B Test Caveats & Advices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Avoid Biased Stopping Times"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NO PEEKING. When running an A/B test, we should avoid stopping the experiment as soon as the results \"look\" significant. Using a stopping time that is dependent upon the results of the experiment can inflate our false-positive rate substantially.\n",
"\n",
"Recall that in many experiments, we set the significance threshold to be 5% (or a p-value threshold of 0.05). This means that we'll accept that Variation A is better than Variation B if A beats B by a margin large enough that a false positive would only happen 5% of the time. If we, however, were to check the experiment with the intent of stopping it if it shows significance, then every time we perform the significance test, we're essentially inflating our false-positive rate. To be more explicit, every time we perform the test there's a 5% chance of false-positive, so in other words, 95% chance of drawing the right conclusion, if we perform it again then that means we need both test to be correct to draw the right conclusion, i.e. the probability of both test giving us the correct result now becomes (1 - 5%)(1 - 5%) and the probability of committing a false positive error is now: 1 - (1 - 5%)(1 - 5%)."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"ExecuteTime": {
"end_time": "2021-04-01T04:02:52.326261Z",
"start_time": "2021-04-01T04:02:52.298864Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"conducting the test 2 times 0.09750000000000003\n",
"conducting the test 10 times 0.4012630607616213\n"
]
}
],
"source": [
"# the false positive rate of conducting the test for n times\n",
"significance = 0.05\n",
"\n",
"print('conducting the test 2 times', 1 - (1 - significance) ** 2)\n",
"print('conducting the test 10 times', 1 - (1 - significance) ** 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The easiest way to avoid this problem is to **choose a stopping time that's independent of the test results**. We could, for example, decide in advance to run the test for a fix amount of time, no matter the results we observed during the test's tenure. Thus just like in the template above, if 99% of our visitors convert after 1 week, then we should do the following.\n",
"\n",
"- Run your test for two weeks.\n",
"- Include in the test only users who show up in the first week. If a user shows up on day 13, you have not given them enough time to convert.\n",
"- At the end of the test, if a user who showed up on day 2 converts more than 7 days after he first arrived, he must be counted as a non-conversion.\n",
"\n",
"Or we could decide to run the test until each bucket has received more than 10,000 visitors, again ignoring the test results until that condition is met. There are tests like power tests that let's us determine how many tests we should run before we make a conclusion about the result. Although we should be very careful with this, because the truth is: It's not really the number of conversions that matters; it's whether the time frame of the test is long enough to capture variations on your site. \n",
"\n",
"For instance, the website traffic may behave one way during the day and another way at night (the same holds on weekdays and weekends). Then it's worth noting that there are two effects that could occur when new features are introduced: **Primacy** and **Novelty** effect.\n",
"\n",
"- Primacy effect occurs when we change something and experienced users may be less efficient until they get used to the new feature, thus giving inherent advantage to the control (original version)\n",
"- Novelty effect. Meaning when users are switched to a new experience, their initial reactions may not be their long-term reactions. In other words, if we are testing a new color for a button, the user may initially love the button and click it more often, just because it's novel, but eventually he/she would get used to the new color and behave as he/she did before. It's important to run the trial long enough to get past the period of the \"shock of the new\".\n",
"\n",
"In summary, we should set a results-independent stopping time (a week) is the easiest and most reliable way to avoid biased stopping times. Note that running the test for at least a week is advised since it'll make sure that the experiment captures the different user behavior of weekdays, weekends and try to avoid holidays ...."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Do Follow Up Tests and Watch your Overall Success Rate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we're running a lot of A/B tests, we should run follow-up tests and pay attention to our base success rate.\n",
"\n",
"Let's talk about these in reverse order. Imagine that we've done everything right. We set our stopping time in advance, and keep it independent from the test results. We set a relatively high success criterion: A probability of at least 95% that the variant is better than the control (formally, $p \\leq 0.05$). We do all of that.\n",
"\n",
"Then We run 100 tests, each with all the rigor just described. In the end, of those 100 tests, 5 of them claims that the variant will beat the control. How many of those variants do we think are really better than the control, though? If we run 20 tests in a row in which the \"best\" variant is worse or statistically indistinguishable from the control, then we should be suspicious when our 21st test comes out positive. If a button-color test failed to elicit a winner six months ago, but did produce one today, we should be skeptical. Why now but not then?\n",
"\n",
"Here's an intuitive way of thinking about this problem. Let's say we have a class of students who each took a 100-item true/false test on a certain subject. Suppose each student chooses randomly on all\n",
"questions. Each student would achieve a random score between 0 and 100, with an average of 50. \n",
"\n",
"Now take only the top scoring 10% of the class, and declaring them \"winners\", give them a second test, on\n",
"which they again choose randomly. They will most likely score less on the second test than the first test. That's because, no matter what they scored on the first test they will still average 50 correct answers in the second test. This is what's called the **regression to the mean**. Meaning that tests which seem to be successful but then lose their uplift over time.\n",
"\n",
"It can be wise to run our A/B tests twice (a validation test). You'll find that doing so helps to eliminate illusory results. If the results of the first test aren’t robust, you’ll see noticeable decay with the second. But, if the uplift is real, you should still see uplift during the second test. This approach isn’t fail-safe but it will help check whether your results are robust. e.g. In a multiple testing, you tried out three variants, B, C, and D against the control A. Variant C won. Don't deploy it fully yet. Drive 50% of your traffic to Variant C and 50% to Variant A (or some modification on this; the percent split is not important as long as you will have reasonable statistical power within an acceptable time period). As this will give you more information about C's true performance relative to A.\n",
"\n",
"Given the situation above, it's better to keep a record of previous tests, when they were run, the variants that were tried, etc. Since these historical record gives you an idea of what's reasonable. Despite the fact that this information is not directly informative of the rates you should expect from future tests (The absolute numbers are extremely time dependent, so the raw numbers that you get today will be completely different than the ones you would have gotten six months later), it gives you an idea of what's plausible in terms of each test's relative performance.\n",
"\n",
"Also, by keeping a record of previous tests, we can avoid: \n",
"\n",
"- Falling into the trap of \"We already tried that\". A hypothesis can be implemented in so many different ways. If you just do one headline test and say \"we tried that,\" you’re really selling yourself short.\n",
"- Not testing continually or not retesting after months or years. Just because you tested a variation in the past doesn’t necessarily mean that those results are going to be valid a year or two from now (Because we have the record of what we did, we can easily reproduce the test)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## False Reporting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's say we deploy a new feature to your product and wish to see if it increases the product's activation rate (or any other metric or KPI that's relevant to you). Currently the baseline of the product's activation rate is somewhere around 40%. After running the test, we realized that it WORKED, the activation went up to 50%. So we're like, YES! we just raised activation by 25%! and we sent this info to the head of product and ask for a raise. \n",
"\n",
"After two months, the head of product comes back to us and said \"you told me you raised the activation rate by 25%, shouldn't this mean that I should see a big jump in the overall activation? What's going on?\" Well, what's going on is, we did raised activation by 25%, but only for user who uses the product's feature. So if only 10 percent of our users use that product, then the overall increase in activation rate will probably only be around 2.5% (25% * 10%). Which is still probably very good, but the expectation that we've set by mis-reporting can get us into trouble."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Seasonality / Not Running it Against the Correct Target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Suppose you have different types of users (or users with different usage patterns) using your product. e.g. business user and students. Then what can happen is your A/B testing will have different result in July versus October. The reason may be in July all your student users are out on vacation (not using your product) and in October after school starts they start using it again. This is simply saying that the weighting of your user population may be different in different times of the year (seasonality). Thus, you should be clear with yourself about who you're targeting."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Non-Randomized Bucketing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double check if we're actually randomly splitting you're users, this will most likely burn us if our system assigns user id to users in a systematical way. e.g. user id whose last two digits are 70 are all from a specific region.\n",
"\n",
"A more quintessential example of non-randomized bucketing is say we have two types of users. Casual users who spend on average 10 dollars per month, and power users who spend on average 100 dollars. When we randomly assign users to treatment or control in our A/B test there's a chance that power users and casual users will not be evenly split between the two groups. As the sample size for our experiment grows, the likelihood of these imbalances between treatment and control shrinks, but it never completely goes away.\n",
"\n",
"Let's say that during our A/B test we had 100 total users. 50 in Group A (Treatment) and 50 in Group B (Control), but Group A had 20 power users and 30 casual users, while Group B had 30 power users and 20 casual users.\n",
"\n",
"This means is that even before there was any experiment, the treatment group average spending was lower than the control groups. The pre-experiment numbers would look like this:\n",
"\n",
"- Treatment Average = 20 Power Users x 100 + 30 Casual Users x 10 = 2300/50 = 46 per user.\n",
"- Control Average = 30 Power Users x 100 + 20 Casual Users x 10 = 3200/50 = 64 per user.\n",
"- Group Difference = 46 - 64 = -18.\n",
"\n",
"The upshot is our treatment has to have an effect greater than 18 per user to even show that it had a positive impact. But what if it caused users to spend 12 more? We'd compare our two groups and it would look like our treatment had an effect of -6, and we'd likely make the wrong call to not launch the recommended items feature."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Others"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Despite its useful functionality, there are still places where A/B testing isn't as useful. For example: \n",
"\n",
"- A/B testing can't tell us if we're missing something. Meaning it can tell you if A performs better B or vice versa, but it can't tell us that if we use C, then it will actually perform better than the former two.\n",
"- Testing out products that people rarely buy. e.g. cars, apartments. It might be too long before the user actually decides to take actions after seeing the information and we might be unaware of the actual motivation.\n",
"- Optimizing for the funnel, rather than the product. Understanding what the customers want so that we can make the product better. Ultimately, we can't simply test our headlines and get people to like our product more.\n",
"- Conflicting test: Two different product teams both deployed new features on your landing page and ran the A/B test at the same period of time. This is more of a organization problem. You should probably require the product teams to register for their test, and make sure that multiple tests on the same stuff are not running at the same time, or else you might be tracking the effect of the other test.\n",
"- Optimizing the wrong metric. The best example is probably noting that higher click through rate doesn't necessary means higher relevance. To be explicit, poor search results means people perform more searches, and thereby click on more ads. While this seems good in the short term, it's terrible in the long term, as users get more and more frustrated with the search engine. A search engine's goal should be to help users find what they want as quickly as possible, and sessions per user (increasing sessions per user means users are satisfied and returning) should probably be the key metric to showcase instead.\n",
"- The following article also contains some nice reads regarding A/B testing. [Blog: Never start with a hypothesis](https://towardsdatascience.com/hypothesis-testing-decoded-for-movers-and-shakers-bfc2bc34da41)\n",
" - The crux of the article is saying we should remember that when formulating hypothesis in the real world, our default action should be the one that we find palatable under ignorance. Hence the idea of incorrectly leaving our cozy comfort zone (default action) should be more painful than the idea of incorrectly sticking to it. i.e. a Type I error should feel worse than a Type II error."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reference\n",
"\n",
"- [Youtube: Beautiful A/B Testing](https://www.youtube.com/watch?v=EvDg7ssY0M8)\n",
"- [Notebook: Statistics for Hackers](http://nbviewer.jupyter.org/github/croach/statistics-for-hackers/blob/master/statistics-for-hackers.ipynb)\n",
"- [Blog: What Are P-Values?](https://prateekvjoshi.com/2013/12/07/what-are-p-values/)\n",
"- [Blog: How to work with A/B test data](https://medium.com/@carsonforter/how-to-work-with-a-b-test-data-96121b89d1a4)\n",
"- [Blog: Interpreting A/B Test using Python](http://okomestudio.net/biboroku/?p=2375)\n",
"- [Blog: So, You Need a Statistically Significant Sample?](http://multithreaded.stitchfix.com/blog/2015/05/26/significant-sample/)\n",
"- [Blog: How to Build a Strong A/B Testing Plan That Gets Results](https://conversionxl.com/how-to-build-a-strong-ab-testing-plan-that-gets-results/)\n",
"- [Blog: A/B testing and Pearson's chi-squared test of independence](http://thestatsgeek.com/2013/07/22/ab-testing/)\n",
"- [Blog: A/B testing - confidence interval for the difference in proportions using R](http://thestatsgeek.com/2014/02/15/ab-testing-confidence-interval-for-the-difference-in-proportions-using-r/)\n",
"- [Blog: Python for Data Analysis Part 23: Point Estimates and Confidence Intervals](http://hamelg.blogspot.com/2015/11/python-for-data-analysis-part-23-point.html)\n",
"- [Notes: MOST winning A/B test results are illusory](http://www.qubit.com/sites/default/files/pdf/mostwinningabtestresultsareillusory_0.pdf)\n",
"- [Notes: Eberly College of Science STAT 414/415 Comparing Two Proportions](https://onlinecourses.science.psu.edu/stat414/node/268)\n",
"- [Quora: When should A/B testing not be trusted to make decisions?](https://www.quora.com/When-should-A-B-testing-not-be-trusted-to-make-decisions)\n",
"- [Forbes: How To Do A/B Testing Right And Avoid The Most Common Mistakes Marketers Make](https://www.forbes.com/sites/sujanpatel/2015/10/29/how-to-do-ab-testing-right-and-avoid-the-most-common-mistakes-marketers-make/)\n",
"- [Paper: R. Kohavi, A. Deng, B. Frasca, R. Longbotham, T. Walker, Y. Xu (2012) Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained](http://notes.stephenholiday.com/Five-Puzzling-Outcomes.pdf)\n",
"- [Slideshare: 4 Steps Toward Scientific A/B Testing](https://www.slideshare.net/RJMetrics/4-steps-toward-scientific-ab-testing)"
]
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