Data Hazard Selections Pre Post Workshops.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ad7ef130",
   "metadata": {},
   "source": [
    "# Data Hazard Selections\n",
    "\n",
    "Participants selected the Hazards they thought applied before and after the workshop discussions. Here we are assessing how this changed between the two timepoints. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1fe0475",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c81e0c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"DataHazards_WorkshopData.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eada23f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event</th>\n",
       "      <th>participant-work</th>\n",
       "      <th>participant-field</th>\n",
       "      <th>participant-type</th>\n",
       "      <th>project</th>\n",
       "      <th>Pre_AutoDecision</th>\n",
       "      <th>Pre_ClassifiesRanks</th>\n",
       "      <th>Pre_LacksCommunity</th>\n",
       "      <th>Pre_DifficultUnderstand</th>\n",
       "      <th>Pre_DirectHarm</th>\n",
       "      <th>...</th>\n",
       "      <th>Post_High environmental cost</th>\n",
       "      <th>Post_InformedConsent</th>\n",
       "      <th>Post_Misuse</th>\n",
       "      <th>Post_Privacy</th>\n",
       "      <th>Post_ReinforcesBiases</th>\n",
       "      <th>eval-clear</th>\n",
       "      <th>eval-easy</th>\n",
       "      <th>eval-hazards-useful</th>\n",
       "      <th>eval-group-useful</th>\n",
       "      <th>eval-learned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sept-21</td>\n",
       "      <td>Higher Education</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Audience member</td>\n",
       "      <td>Project 1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Very clear</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>Very useful</td>\n",
       "      <td>Very useful</td>\n",
       "      <td>Very much</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sept-21</td>\n",
       "      <td>Higher Education</td>\n",
       "      <td>Science,Health Sciences,Life Sciences</td>\n",
       "      <td>Project owner (presenting a project)</td>\n",
       "      <td>Project 1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Very clear</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Somewhat useful</td>\n",
       "      <td>Somewhat useful</td>\n",
       "      <td>Somewhat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sept-21</td>\n",
       "      <td>Higher Education,Private/Public Sector,Data Sc...</td>\n",
       "      <td>Mathematics,Health Sciences</td>\n",
       "      <td>Audience member</td>\n",
       "      <td>Project 1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Very clear</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>Very useful</td>\n",
       "      <td>Somewhat useful</td>\n",
       "      <td>Very much</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sept-21</td>\n",
       "      <td>Higher Education</td>\n",
       "      <td>Ethicist</td>\n",
       "      <td>Audience member</td>\n",
       "      <td>Project 1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Somewhat clear</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>Very useful</td>\n",
       "      <td>Somewhat useful</td>\n",
       "      <td>Very much</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sept-21</td>\n",
       "      <td>Higher Education</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Audience member</td>\n",
       "      <td>Project 1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Very clear</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Very useful</td>\n",
       "      <td>Somewhat useful</td>\n",
       "      <td>Very much</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     event                                   participant-work  \\\n",
       "0  sept-21                                   Higher Education   \n",
       "1  sept-21                                   Higher Education   \n",
       "2  sept-21  Higher Education,Private/Public Sector,Data Sc...   \n",
       "3  sept-21                                   Higher Education   \n",
       "4  sept-21                                   Higher Education   \n",
       "\n",
       "                       participant-field  \\\n",
       "0                            Engineering   \n",
       "1  Science,Health Sciences,Life Sciences   \n",
       "2            Mathematics,Health Sciences   \n",
       "3                               Ethicist   \n",
       "4                            Engineering   \n",
       "\n",
       "                       participant-type    project  Pre_AutoDecision  \\\n",
       "0                       Audience member  Project 1               1.0   \n",
       "1  Project owner (presenting a project)  Project 1               0.0   \n",
       "2                       Audience member  Project 1               1.0   \n",
       "3                       Audience member  Project 1               0.0   \n",
       "4                       Audience member  Project 1               1.0   \n",
       "\n",
       "   Pre_ClassifiesRanks  Pre_LacksCommunity  Pre_DifficultUnderstand  \\\n",
       "0                  1.0                 0.0                      0.0   \n",
       "1                  1.0                 0.0                      0.0   \n",
       "2                  1.0                 1.0                      1.0   \n",
       "3                  1.0                 1.0                      1.0   \n",
       "4                  1.0                 0.0                      1.0   \n",
       "\n",
       "   Pre_DirectHarm  ...  Post_High environmental cost  Post_InformedConsent  \\\n",
       "0             0.0  ...                           1.0                   0.0   \n",
       "1             0.0  ...                           0.0                   0.0   \n",
       "2             1.0  ...                           0.0                   1.0   \n",
       "3             0.0  ...                           0.0                   1.0   \n",
       "4             1.0  ...                           0.0                   0.0   \n",
       "\n",
       "   Post_Misuse  Post_Privacy  Post_ReinforcesBiases      eval-clear  \\\n",
       "0          1.0           1.0                    1.0      Very clear   \n",
       "1          1.0           1.0                    1.0      Very clear   \n",
       "2          1.0           1.0                    1.0      Very clear   \n",
       "3          1.0           1.0                    0.0  Somewhat clear   \n",
       "4          1.0           1.0                    1.0      Very clear   \n",
       "\n",
       "       eval-easy  eval-hazards-useful  eval-group-useful  eval-learned  \n",
       "0      Very easy          Very useful        Very useful     Very much  \n",
       "1  Somewhat easy      Somewhat useful    Somewhat useful      Somewhat  \n",
       "2      Very easy          Very useful    Somewhat useful     Very much  \n",
       "3      Very easy          Very useful    Somewhat useful     Very much  \n",
       "4  Somewhat easy          Very useful    Somewhat useful     Very much  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b94d967",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre = data.filter(regex='Pre_')\n",
    "post = data.filter(regex='Post_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b366bf7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a column counting the before and after for both. Must have all valid values to be counted.\n",
    "data['Pre_Count'] = pre.sum(axis=1, min_count=11)\n",
    "data['Post_Count'] = post.sum(axis=1, min_count=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "46020d93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subset relevant columns and only rows with no missing data\n",
    "counts = data[['participant-type', 'project', 'Pre_Count', 'Post_Count']]\n",
    "# Subset for only audience members\n",
    "counts = counts.loc[counts['participant-type'] == \"Audience member\"]\n",
    "# Only keep rows with no missing data\n",
    "counts = counts.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d923d94f",
   "metadata": {},
   "source": [
    "## Describing the difference in choices by project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "407043aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tx/67h5cbx52_3djdg95lvy51fm0000gq/T/ipykernel_68983/1783414304.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  counts.groupby('project')['Pre_Count', 'Post_Count'].mean()\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pre_Count</th>\n",
       "      <th>Post_Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>project</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Project 1</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Project 2</th>\n",
       "      <td>5.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Project 3</th>\n",
       "      <td>5.200000</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Project 4</th>\n",
       "      <td>4.866667</td>\n",
       "      <td>5.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Project 5</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>6.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pre_Count  Post_Count\n",
       "project                         \n",
       "Project 1   6.500000    7.666667\n",
       "Project 2   5.200000    6.000000\n",
       "Project 3   5.200000    5.800000\n",
       "Project 4   4.866667    5.266667\n",
       "Project 5   7.400000    6.800000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.groupby('project')['Pre_Count', 'Post_Count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f76fe40e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>participant-type</th>\n",
       "      <th>Pre_Count</th>\n",
       "      <th>Post_Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>project</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Project 1</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Project 2</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Project 3</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Project 4</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Project 5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           participant-type  Pre_Count  Post_Count\n",
       "project                                           \n",
       "Project 1                 6          6           6\n",
       "Project 2                 5          5           5\n",
       "Project 3                 5          5           5\n",
       "Project 4                15         15          15\n",
       "Project 5                 5          5           5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counts of participants in each group\n",
    "counts.groupby('project').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2deb0f1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of observations included total\n",
    "len(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b8033c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.583333333333333"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean number of Hazards before\n",
    "counts['Pre_Count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d8c4bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.055555555555555"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean number of Hazards after\n",
    "counts['Post_Count'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2dfde63",
   "metadata": {},
   "source": [
    "## Formally testing the difference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01fa2c6d",
   "metadata": {},
   "source": [
    "Using a one tailed paired students t-test. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cf0ebd07",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8147e00",
   "metadata": {},
   "source": [
    "First just do a quick visual check of the distributions. The sample is small (36) so we're not expecting there to be a perfectly normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "03873d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1., 6., 6., 5., 0., 5., 6., 5., 0., 2.]),\n",
       " array([ 2. ,  2.8,  3.6,  4.4,  5.2,  6. ,  6.8,  7.6,  8.4,  9.2, 10. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(counts['Pre_Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3daf6c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1., 1., 1., 8., 4., 4., 8., 5., 1., 3.]),\n",
       " array([ 0. ,  1.1,  2.2,  3.3,  4.4,  5.5,  6.6,  7.7,  8.8,  9.9, 11. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAMj0lEQVR4nO3dYazddX3H8ffHFgKtGNx6ZrTl7mJm2AyJQm4Y2oRslC2wGtiDLSkJxhmT+8QpGBNT94TsGUuM0QfG7AZRElmNqyUzdGMYlRiTrVtb2GwpZA4rFNBeYhzgliHuuwfnXLheTr3/tud/z6+371fS9N5zT+/9HFreHP73/PtPVSFJatcbpj1AkvSrGWpJapyhlqTGGWpJapyhlqTGbezjk27ZsqVmZ2f7+NSStC4dOnTo+aoajPtYL6GenZ3l4MGDfXxqSVqXkvzwVB/z0IckNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjOoU6yceSHE1yJMmeJBf1PUySNLRqqJNsBT4KzFXVlcAGYFffwyRJQ10PfWwELk6yEdgEPNvfJEnScquemVhVzyT5FPAU8D/AQ1X10Mr7JZkH5gFmZmYmvVM9mN29f2pf+/hdO6f2tafBf9Y6G10OfbwZuAW4HHgbsDnJbSvvV1ULVTVXVXODwdjT1SVJZ6DLoY8bgB9U1WJV/RzYB7y331mSpCVdQv0UcG2STUkC7ACO9TtLkrRk1VBX1QFgL3AY+N7o1yz0vEuSNNLprzmtqjuBO3veIkkawzMTJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGtfl4rZXJHl02Y8XktyxBtskSXS4wktVPQG8GyDJBuAZ4P5+Z0mSlpzuoY8dwH9W1Q/7GCNJer3TDfUuYE8fQyRJ43UOdZILgZuBvz3Fx+eTHExycHFxcVL7JOm8dzrPqG8CDlfVj8d9sKoWqmququYGg8Fk1kmSTivUt+JhD0lac51CnWQT8AfAvn7nSJJWWvXleQBV9d/Ar/e8RZI0hmcmSlLjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1Ljul6K69Ike5M8nuRYkvf0PUySNNTpUlzAZ4EHq+pPklwIbOpxkyRpmVVDneRNwHXAnwFU1cvAy/3OkiQt6XLo4+3AIvDFJI8kuTvJ5pV3SjKf5GCSg4uLixMfKknnqy6h3ghcDXy+qq4CfgbsXnmnqlqoqrmqmhsMBhOeKUnnry6hPgGcqKoDo/f3Mgy3JGkNrBrqqvoR8HSSK0Y37QAe63WVJOlVXV/18RHgvtErPp4EPtjfJEnScp1CXVWPAnP9TpEkjeOZiZLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUuE5XeElyHHgR+AXwSlV5tRdJWiNdr5kI8PtV9XxvSyRJY3noQ5Ia1/UZdQEPJSngr6tqYeUdkswD8wAzMzOTWyjprMzu3j+Vr3v8rp1T+brrUddn1Nur6mrgJuDDSa5beYeqWqiquaqaGwwGEx0pSeezTqGuqmdHP58E7geu6XOUJOk1q4Y6yeYklyy9DfwhcKTvYZKkoS7HqN8C3J9k6f5/U1UP9rpKkvSqVUNdVU8C71qDLZKkMXx5niQ1zlBLUuMMtSQ1zlBLUuMMtSQ1zlBLUuMMtSQ1zlBLUuMMtSQ1zlBLUuMMtSQ1zlBLUuMMtSQ1zlBLUuMMtSQ1zlBLUuMMtSQ1rnOok2xI8kiSB/ocJEn6ZafzjPp24FhfQyRJ43UKdZJtwE7g7n7nSJJW6nIVcoDPAJ8ALjnVHZLMA/MAMzMzZz1M69vs7v3TniCdM1Z9Rp3kfcDJqjr0q+5XVQtVNVdVc4PBYGIDJel81+XQx3bg5iTHga8A1yf5cq+rJEmvWjXUVfXJqtpWVbPALuBbVXVb78skSYCvo5ak5nX9ZiIAVfUw8HAvSyRJY/mMWpIaZ6glqXGGWpIaZ6glqXGGWpIaZ6glqXGGWpIaZ6glqXGGWpIaZ6glqXGGWpIaZ6glqXGGWpIaZ6glqXGGWpIaZ6glqXGGWpIa1+Uq5Bcl+Zck/5bkaJK/XIthkqShLpfi+l/g+qp6KckFwHeT/ENV/XPP2yRJdAh1VRXw0ujdC0Y/qs9RkqTXdLq4bZINwCHgt4DPVdWBMfeZB+YBZmZmJrlRkk7L7O79U/m6x+/a2cvn7fTNxKr6RVW9G9gGXJPkyjH3WaiquaqaGwwGE54pSeev03rVR1X9FHgYuLGPMZKk1+vyqo9BkktHb18M3AA83vMuSdJIl2PUbwXuHR2nfgPw1ap6oN9ZkqQlXV718e/AVWuwRZI0hmcmSlLjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1Ljulwz8bIk305yLMnRJLevxTBJ0lCXaya+Any8qg4nuQQ4lOQbVfVYz9skSXR4Rl1Vz1XV4dHbLwLHgK19D5MkDZ3WMeokswwvdHtgzMfmkxxMcnBxcXFC8yRJnUOd5I3A14A7quqFlR+vqoWqmququcFgMMmNknRe6xTqJBcwjPR9VbWv30mSpOW6vOojwBeAY1X16f4nSZKW6/KMejvwfuD6JI+OfvxRz7skSSOrvjyvqr4LZA22SJLG8MxESWqcoZakxhlqSWqcoZakxhlqSWqcoZakxhlqSWqcoZakxhlqSWqcoZakxhlqSWqcoZakxhlqSWqcoZakxhlqSWqcoZakxhlqSWpcl2sm3pPkZJIjazFIkvTLujyj/hJwY887JEmnsGqoq+o7wE/WYIskaYxVL27bVZJ5YB5gZmbmjD/P7O79k5okaYr8d3lyJvbNxKpaqKq5qpobDAaT+rSSdN7zVR+S1DhDLUmN6/LyvD3APwFXJDmR5EP9z5IkLVn1m4lVdetaDJEkjeehD0lqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqXKdQJ7kxyRNJvp9kd9+jJEmv6XLNxA3A54CbgHcCtyZ5Z9/DJElDXZ5RXwN8v6qerKqXga8At/Q7S5K0ZNWL2wJbgaeXvX8C+N2Vd0oyD8yP3n0pyRNnuGkL8PwZ/trW+djOXev58fnYJiR/dVa//DdP9YEuoc6Y2+p1N1QtAAunMWr8F0sOVtXc2X6eFvnYzl3r+fH52NrX5dDHCeCyZe9vA57tZ44kaaUuof5X4B1JLk9yIbAL+Hq/syRJS1Y99FFVryT5c+AfgQ3APVV1tMdNZ334pGE+tnPXen58PrbGpep1h5slSQ3xzERJapyhlqTGNRPq9XyaepLLknw7ybEkR5PcPu1Nk5ZkQ5JHkjww7S2TlOTSJHuTPD76/XvPtDdNUpKPjf5MHkmyJ8lF0950ppLck+RkkiPLbvu1JN9I8h+jn988zY1nqolQnwenqb8CfLyqfge4FvjwOnt8ALcDx6Y9ogefBR6sqt8G3sU6eoxJtgIfBeaq6kqGLxbYNd1VZ+VLwI0rbtsNfLOq3gF8c/T+OaeJULPOT1Ovqueq6vDo7RcZ/su+dbqrJifJNmAncPe0t0xSkjcB1wFfAKiql6vqp1MdNXkbgYuTbAQ2cQ6fI1FV3wF+suLmW4B7R2/fC/zxWm6alFZCPe409XUTsuWSzAJXAQemPGWSPgN8Avi/Ke+YtLcDi8AXR4d17k6yedqjJqWqngE+BTwFPAf8V1U9NN1VE/eWqnoOhk+YgN+Y8p4z0kqoO52mfq5L8kbga8AdVfXCtPdMQpL3ASer6tC0t/RgI3A18Pmqugr4Gefo/zqPMzpeewtwOfA2YHOS26a7SuO0Eup1f5p6kgsYRvq+qto37T0TtB24Oclxhoesrk/y5elOmpgTwImqWvq/n70Mw71e3AD8oKoWq+rnwD7gvVPeNGk/TvJWgNHPJ6e854y0Eup1fZp6kjA8znmsqj497T2TVFWfrKptVTXL8PftW1W1Lp6VVdWPgKeTXDG6aQfw2BQnTdpTwLVJNo3+jO5gHX2zdOTrwAdGb38A+LspbjljXf72vN5N4TT1tbYdeD/wvSSPjm77i6r6++lNUkcfAe4bPYF4EvjglPdMTFUdSLIXOMzwlUmPcA6fcp1kD/B7wJYkJ4A7gbuAryb5EMP/MP3p9BaeOU8hl6TGtXLoQ5J0CoZakhpnqCWpcYZakhpnqCWpcYZakhpnqCWpcf8PuxTVXQ+QxTUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(counts['Post_Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "891025b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-1.3921604610050047, pvalue=0.08632855072434303)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run the ttest - specify alternative hypothesis that the mean of pre is less than the mean of post \n",
    "stats.ttest_rel(counts['Pre_Count'], counts['Post_Count'], alternative = 'less')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dh-analysis",
   "language": "python",
   "name": "dh-analysis"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}