{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayesian Estimation Supersedes the T-Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: typing-extensions>=3.7.4 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from pymc) (4.11.0)\n",
      "Requirement already satisfied: setuptools>=60.0.0 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from arviz>=0.13.0->pymc) (75.1.0)\n",
      "Requirement already satisfied: matplotlib>=3.5 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from arviz>=0.13.0->pymc) (3.9.2)\n",
      "Requirement already satisfied: packaging in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from arviz>=0.13.0->pymc) (24.1)\n",
      "Requirement already satisfied: xarray>=2022.6.0 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from arviz>=0.13.0->pymc) (2025.1.1)\n",
      "Requirement already satisfied: h5netcdf>=1.0.2 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from arviz>=0.13.0->pymc) (1.4.1)\n",
      "Requirement already satisfied: xarray-einstats>=0.3 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from arviz>=0.13.0->pymc) (0.8.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from pandas>=0.24.0->pymc) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from pandas>=0.24.0->pymc) (2024.1)\n",
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      "Requirement already satisfied: filelock>=3.15 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from pytensor<2.27,>=2.26.1->pymc) (3.17.0)\n",
      "Requirement already satisfied: etuples in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from pytensor<2.27,>=2.26.1->pymc) (0.3.9)\n",
      "Requirement already satisfied: logical-unification in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from pytensor<2.27,>=2.26.1->pymc) (0.4.6)\n",
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      "Requirement already satisfied: cons in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from pytensor<2.27,>=2.26.1->pymc) (0.4.6)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from rich>=13.7.1->pymc) (3.0.0)\n",
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      "Requirement already satisfied: h5py in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from h5netcdf>=1.0.2->arviz>=0.13.0->pymc) (3.12.1)\n",
      "Requirement already satisfied: mdurl~=0.1 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from markdown-it-py>=2.2.0->rich>=13.7.1->pymc) (0.1.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from matplotlib>=3.5->arviz>=0.13.0->pymc) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from matplotlib>=3.5->arviz>=0.13.0->pymc) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from matplotlib>=3.5->arviz>=0.13.0->pymc) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from matplotlib>=3.5->arviz>=0.13.0->pymc) (1.4.4)\n",
      "Requirement already satisfied: pillow>=8 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from matplotlib>=3.5->arviz>=0.13.0->pymc) (11.0.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from matplotlib>=3.5->arviz>=0.13.0->pymc) (3.2.0)\n",
      "Requirement already satisfied: six>=1.5 in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas>=0.24.0->pymc) (1.16.0)\n",
      "Requirement already satisfied: toolz in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from logical-unification->pytensor<2.27,>=2.26.1->pymc) (1.0.0)\n",
      "Requirement already satisfied: multipledispatch in /Users/andrew/anaconda3/envs/pm21-dragon/lib/python3.11/site-packages (from logical-unification->pytensor<2.27,>=2.26.1->pymc) (1.0.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pymc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An in-browser version at http://sumsar.net/best_online/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on PyMC v5.20.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pymc as pm\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-v0_8-darkgrid')\n",
    "print('Running on PyMC v{}'.format(pm.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook from https://docs.pymc.io/en/v3/pymc-examples/examples/case_studies/BEST.html . The original implementation was by Andrew Straw, ported to PyMC3 by Thomas Wiecki (c) 2015, and updated by Chris Fonnesbeck."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model replicates the example used in:\n",
    "Kruschke, John. (2012) **Bayesian estimation supersedes the t-test**. *Journal of Experimental Psychology*: General. https://pubmed.ncbi.nlm.nih.gov/22774788/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Problem\n",
    "\n",
    "Several statistical inference procedures involve the comparison of two groups. We may be interested in whether one group is larger than another, or simply different from the other. We require a statistical model for this because true differences are usually accompanied by measurement or stochastic noise that prevent us from drawing conclusions simply from differences calculated from the observed data. \n",
    "\n",
    "The *de facto* standard for statistically comparing two (or more) samples is to use a statistical test. This involves expressing a null hypothesis, which typically claims that there is no difference between the groups, and using a chosen test statistic to determine whether the distribution of the observed data is plausible under the hypothesis. This rejection occurs when the calculated test statistic is higher than some pre-specified threshold value.\n",
    "\n",
    "Unfortunately, it is not easy to conduct hypothesis tests correctly, and their results are very easy to misinterpret. Setting up a statistical test involves several subjective choices (*e.g.* statistical test to use, null hypothesis to test, significance level) by the user that are rarely justified based on the problem or decision at hand, but rather, are usually based on traditional choices that are entirely arbitrary (Johnson 1999). The evidence that it provides to the user is indirect, incomplete, and typically overstates the evidence against the null hypothesis (Goodman 1999). \n",
    "\n",
    "A more informative and effective approach for comparing groups is one based on **estimation** rather than **testing**, and is driven by Bayesian probability rather than frequentist. That is, rather than testing whether two groups are different, we instead pursue an estimate of how different they are, which is fundamentally more informative. Moreover, we include an estimate of uncertainty associated with that difference which includes uncertainty due to our lack of knowledge of the model parameters (epistemic uncertainty) and uncertainty due to the inherent stochasticity of the system (aleatory uncertainty)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Drug trial evaluation\n",
    "\n",
    "To illustrate how this Bayesian estimation approach works in practice, we will use a fictitious example from Kruschke (2012) concerning the evaluation of a clinical trial for drug evaluation. The trial aims to evaluate the efficacy of a \"smart drug\" that is supposed to increase intelligence by comparing IQ scores of individuals in a treatment arm (those receiving the drug) to those in a control arm (those recieving a placebo). There are 47 individuals and 42 individuals in the treatment and control arms, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drug = (101,100,102,104,102,97,105,105,98,101,100,107,123,105,103,100,95,102,106,\n",
    "        109,102,113,102,100,102,102,101,102,106,102,103,111)\n",
    "\n",
    "placebo = (99,101,100,101,102,100,97,101,104,101,102,102,100,105,101,100,\n",
    "           104,100,100,100,101,102,103,97)\n",
    "\n",
    "y1 = np.array(drug)\n",
    "y2 = np.array(placebo)\n",
    "y = pd.DataFrame(dict(value=np.r_[y1, y2], group=np.r_[['drug']*len(drug), ['placebo']*len(placebo)]))\n",
    "\n",
    "y.hist('value', by='group', figsize=(12, 4));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step in a Bayesian approach to inference is to specify the full probability model that corresponds to the problem. For this example, Kruschke chooses a Student-t distribution to describe the distributions of the scores in each group. This choice adds robustness to the analysis, as a T distribution is less sensitive to outlier observations, relative to a normal distribution. The three-parameter Student-t distribution allows for the specification of a mean $\\mu$, a precision (inverse-variance) $\\lambda$ and a degrees-of-freedom parameter $\\nu$:\n",
    "\n",
    "$$f(x|\\mu,\\lambda,\\nu) = \\frac{\\Gamma(\\frac{\\nu + 1}{2})}{\\Gamma(\\frac{\\nu}{2})} \\left(\\frac{\\lambda}{\\pi\\nu}\\right)^{\\frac{1}{2}} \\left[1+\\frac{\\lambda(x-\\mu)^2}{\\nu}\\right]^{-\\frac{\\nu+1}{2}}$$\n",
    "           \n",
    "the degrees-of-freedom parameter essentially specifies the \"normality\" of the data, since larger values of $\\nu$ make the distribution converge to a normal distribution, while small values (close to zero) result in heavier tails.\n",
    "\n",
    "Thus, the likelihood functions of our model are specified as follows:\n",
    "\n",
    "$$y^{(treat)}_i \\sim T(\\nu, \\mu_1, \\sigma_1)$$\n",
    "\n",
    "$$y^{(placebo)}_i \\sim T(\\nu, \\mu_2, \\sigma_2)$$\n",
    "\n",
    "As a simplifying assumption, we will assume that the degree of normality $\\nu$ is the same for both groups. We will, of course, have separate parameters for the means $\\mu_k, k=1,2$ and standard deviations $\\sigma_k$.\n",
    "\n",
    "Since the means are real-valued, we will apply normal priors on them, and arbitrarily set the hyperparameters to the pooled empirical mean of the data and twice the pooled empirical standard deviation, which applies very diffuse information to these quantities (and importantly, does not favor one or the other *a priori*).\n",
    "\n",
    "$$\\mu_k \\sim N(\\bar{x}, 2s)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "μ_m = y.value.mean()\n",
    "μ_s = y.value.std() * 2\n",
    "\n",
    "with pm.Model() as model:\n",
    "    group1_mean = pm.Normal('group1_mean', mu=μ_m, sigma=μ_s)\n",
    "    group2_mean = pm.Normal('group2_mean', mu=μ_m, sigma=μ_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The group standard deviations will be given a uniform prior over a plausible range of values for the variability of the outcome variable, IQ.\n",
    "\n",
    "In Kruschke's original model, he uses a very wide uniform prior for the group standard deviations, from the pooled empirical standard deviation divided by 1000 to the pooled standard deviation multiplied by 1000. This is a poor choice of prior, because very basic prior knowledge about measures of human coginition dictate that the variation cannot ever be as high as this upper bound. IQ is a standardized measure, and hence this constrains how variable a given population's IQ values can be. When you place such a wide uniform prior on these values, you are essentially giving a lot of prior weight on inadmissable values. In this example, there is little practical difference, but in general it is best to apply as much prior information that you have available to the parameterization of prior distributions. \n",
    "\n",
    "We will instead set the group standard deviations to have a $\\text{Uniform}(1,10)$ prior:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "σ_low = 1\n",
    "σ_high = 10\n",
    "\n",
    "with model:\n",
    "    group1_std = pm.Uniform('group1_std', lower=σ_low, upper=σ_high)\n",
    "    group2_std = pm.Uniform('group2_std', lower=σ_low, upper=σ_high)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We follow Kruschke by making the prior for $\\nu$ exponentially distributed with a mean of 30; this allocates high prior probability over the regions of the parameter that describe the range from normal to heavy-tailed data under the Student-T distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with model:\n",
    "    ν = pm.Exponential('ν_minus_one', 1/29.) + 1\n",
    "\n",
    "pm.plot_kde(np.random.exponential(30, size=10000), fill_kwargs={'alpha': 0.5});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since PyMC parameterizes the Student-T in terms of precision, rather than standard deviation, we must transform the standard deviations before specifying our likelihoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with model:\n",
    "    λ1 = group1_std**-2\n",
    "    λ2 = group2_std**-2\n",
    "\n",
    "    group1 = pm.StudentT('drug', nu=ν, mu=group1_mean, lam=λ1, observed=y1)\n",
    "    group2 = pm.StudentT('placebo', nu=ν, mu=group2_mean, lam=λ2, observed=y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having fully specified our probabilistic model, we can turn our attention to calculating the comparisons of interest in order to evaluate the effect of the drug. To this end, we can specify deterministic nodes in our model for the difference between the group means and the difference between the group standard deviations. Wrapping them in named `Deterministic` objects signals to PyMC that we wish to record the sampled values as part of the output.\n",
    "\n",
    "As a joint measure of the groups, we will also estimate the \"effect size\", which is the difference in means scaled by the pooled estimates of standard deviation. This quantity can be harder to interpret, since it is no longer in the same units as our data, but the quantity is a function of all four estimated parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with model:\n",
    "    diff_of_means = pm.Deterministic('difference of means', group1_mean - group2_mean)\n",
    "    diff_of_stds = pm.Deterministic('difference of stds', group1_std - group2_std)\n",
    "    effect_size = pm.Deterministic('effect size', \n",
    "                                   diff_of_means / np.sqrt((group1_std**2 + group2_std**2) / 2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can fit the model and evaluate its output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [group1_mean, group2_mean, group1_std, group2_std, ν_minus_one]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "03855614d33e431a89d0f401c1606162",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 2 seconds.\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    trace = pm.sample(2000, return_inferencedata=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the stochastic parameters of the model. PyMC's `plot_posterior` function replicates the informative histograms portrayed in Kruschke (2012). These summarize the posterior distributions of the parameters, and present a 95% credible interval and the posterior mean. The plots below are constructed with the final 1000 samples from each of the 2 chains, pooled together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2208x1104 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.plot_posterior(trace, var_names=['group1_mean','group2_mean', 'group1_std', 'group2_std', 'ν_minus_one'],\n",
    "                  color='#87ceeb');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2208x552 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.plot_posterior(trace, var_names=['difference of means','difference of stds', 'effect size'],\n",
    "                  ref_val=0,\n",
    "                  color='#87ceeb');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the group differences, we can conclude that there are meaningful differences between the two groups for all three measures. For these comparisons, it is useful to use zero as a reference value (`ref_val`); providing this reference value yields cumulative probabilities for the posterior distribution on either side of the value. Thus, for the difference in means, 99.4% of the posterior probability is greater than zero, which suggests the group means are credibly different. The effect size and differences in standard deviation are similarly positive.\n",
    "\n",
    "These estimates suggest that the \"smart drug\" increased both the expected scores, but also the  variability in scores across the sample. So, this does not rule out the possibility that some recipients may be adversely affected by the drug at the same time others benefit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>group1_mean</th>\n",
       "      <td>102.636</td>\n",
       "      <td>0.741</td>\n",
       "      <td>101.224</td>\n",
       "      <td>103.997</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.007</td>\n",
       "      <td>5976.0</td>\n",
       "      <td>5106.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group2_mean</th>\n",
       "      <td>100.916</td>\n",
       "      <td>0.385</td>\n",
       "      <td>100.191</td>\n",
       "      <td>101.630</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.003</td>\n",
       "      <td>7313.0</td>\n",
       "      <td>5440.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group1_std</th>\n",
       "      <td>3.450</td>\n",
       "      <td>0.898</td>\n",
       "      <td>1.867</td>\n",
       "      <td>5.141</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.010</td>\n",
       "      <td>4050.0</td>\n",
       "      <td>4513.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group2_std</th>\n",
       "      <td>1.632</td>\n",
       "      <td>0.365</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.248</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>3668.0</td>\n",
       "      <td>2350.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ν_minus_one</th>\n",
       "      <td>6.342</td>\n",
       "      <td>11.123</td>\n",
       "      <td>0.073</td>\n",
       "      <td>19.650</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.120</td>\n",
       "      <td>3798.0</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difference of means</th>\n",
       "      <td>1.721</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.123</td>\n",
       "      <td>3.201</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>6484.0</td>\n",
       "      <td>5471.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difference of stds</th>\n",
       "      <td>1.818</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.298</td>\n",
       "      <td>3.458</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.008</td>\n",
       "      <td>5269.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>effect size</th>\n",
       "      <td>0.650</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.075</td>\n",
       "      <td>1.207</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>7690.0</td>\n",
       "      <td>5837.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        mean      sd   hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "group1_mean          102.636   0.741  101.224  103.997      0.010    0.007   \n",
       "group2_mean          100.916   0.385  100.191  101.630      0.005    0.003   \n",
       "group1_std             3.450   0.898    1.867    5.141      0.014    0.010   \n",
       "group2_std             1.632   0.365    1.000    2.248      0.005    0.004   \n",
       "ν_minus_one            6.342  11.123    0.073   19.650      0.170    0.120   \n",
       "difference of means    1.721   0.821    0.123    3.201      0.010    0.008   \n",
       "difference of stds     1.818   0.844    0.298    3.458      0.012    0.008   \n",
       "effect size            0.650   0.304    0.075    1.207      0.003    0.002   \n",
       "\n",
       "                     ess_bulk  ess_tail  r_hat  \n",
       "group1_mean            5976.0    5106.0    1.0  \n",
       "group2_mean            7313.0    5440.0    1.0  \n",
       "group1_std             4050.0    4513.0    1.0  \n",
       "group2_std             3668.0    2350.0    1.0  \n",
       "ν_minus_one            3798.0    4765.0    1.0  \n",
       "difference of means    6484.0    5471.0    1.0  \n",
       "difference of stds     5269.0    5200.0    1.0  \n",
       "effect size            7690.0    5837.0    1.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm.summary(trace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "Goodman SN. Toward evidence-based medical statistics. 1: The P value fallacy. Annals of Internal Medicine. 1999;130(12):995-1004. doi:10.7326/0003-4819-130-12-199906150-00008.\n",
    "\n",
    "Johnson D. The insignificance of statistical significance testing. Journal of Wildlife Management. 1999;63(3):763-772.\n",
    "\n",
    "Kruschke JK. Bayesian estimation supersedes the t test. J Exp Psychol Gen. 2013;142(2):573-603. doi:10.1037/a0029146."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original pymc2 implementation was written by Andrew Straw and can be found here: https://github.com/strawlab/best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Beyond BEST\n",
    "\n",
    "There are numerous, high-quality examples in [the PyMC example gallery](https://www.pymc.io/projects/examples/en/latest/gallery.html)."
   ]
  }
 ],
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