{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "278f1daf",
   "metadata": {},
   "source": [
    "# Paired Samples t-test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "741220b6",
   "metadata": {},
   "source": [
    "### Set up Python libraries\n",
    "\n",
    "As usual, run the code cell below to import the relevant Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "692abf91",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set-up Python libraries - you need to run this but you don't need to change it\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_theme(style='white')\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import warnings \n",
    "warnings.simplefilter('ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c9ed34",
   "metadata": {},
   "source": [
    "## Example\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook/main/images/horror.jpg\" width=30% alt=\"There should be a picture from a horror movie here\" >\n",
    "\n",
    "A scientist hypothesises that watching horror movies raises the heart rate in human subjects. \n",
    "She measures the heart rate of 20 volunteers watching a horror movie, \n",
    "and the same volunteers watching a cookery show.\n",
    "\n",
    "This is a <b>repeated measures</b> design, which is a form of paired design"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9edf419d",
   "metadata": {},
   "source": [
    "### Inspect the data\n",
    "\n",
    "Let's load the data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f18b874",
   "metadata": {
    "tags": []
   },
   "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>cookery</th>\n",
       "      <th>horror</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60.4</td>\n",
       "      <td>72.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.9</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54.4</td>\n",
       "      <td>68.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.0</td>\n",
       "      <td>57.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67.7</td>\n",
       "      <td>58.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56.2</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>61.9</td>\n",
       "      <td>71.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>58.9</td>\n",
       "      <td>62.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>65.6</td>\n",
       "      <td>68.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>54.6</td>\n",
       "      <td>73.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>85.2</td>\n",
       "      <td>93.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>87.8</td>\n",
       "      <td>94.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>90.5</td>\n",
       "      <td>111.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>92.7</td>\n",
       "      <td>89.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>85.4</td>\n",
       "      <td>97.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>77.5</td>\n",
       "      <td>90.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>81.3</td>\n",
       "      <td>83.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>79.7</td>\n",
       "      <td>86.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>96.8</td>\n",
       "      <td>90.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>81.9</td>\n",
       "      <td>75.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cookery  horror\n",
       "0      60.4    72.9\n",
       "1      53.9    57.0\n",
       "2      54.4    68.3\n",
       "3      60.0    57.4\n",
       "4      67.7    58.7\n",
       "5      56.2    47.0\n",
       "6      61.9    71.8\n",
       "7      58.9    62.1\n",
       "8      65.6    68.6\n",
       "9      54.6    73.8\n",
       "10     85.2    93.1\n",
       "11     87.8    94.8\n",
       "12     90.5   111.4\n",
       "13     92.7    89.7\n",
       "14     85.4    97.4\n",
       "15     77.5    90.9\n",
       "16     81.3    83.9\n",
       "17     79.7    86.9\n",
       "18     96.8    90.1\n",
       "19     81.9    75.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load the data and have a look\n",
    "heartRates = pd.read_csv('https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook_2024/main/data/HeartRates.csv')\n",
    "display(heartRates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9d1c259",
   "metadata": {},
   "source": [
    "### Scatterplot\n",
    "\n",
    "In the case of paired data, the most effective way to get a sense of the data is a scatterplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7487e866",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data = heartRates, x=\"cookery\", y=\"horror\")\n",
    "plt.xlabel('heart rate: cookery')\n",
    "plt.ylabel('heart rate: horror')\n",
    "\n",
    "# add the line x=y (ie a line from point(50,50) to (110,110)) for reference \n",
    "plt.plot([50,110],[50,110],'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337e7c00",
   "metadata": {},
   "source": [
    "It looks like\n",
    "<ul>\n",
    "    <li> For most individuals heart rate is higher during the horror show (most data points lie above the line x=y)\n",
    "    <li> There is a strong effect of individual - people with low heart rates during the cookery show also have low heart rates during the horror show (hence data points are stretched out along the line x=y)   \n",
    "</ul>\n",
    "\n",
    "It looks like individual differences in heart rate rather dwarf the effect of the type of TV show being watched. \n",
    "Therefore it is a good thing that we used a paired design, in which these individual differences are controlled for (as we only look at the change in heart rate between conditions for each individual)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a939204",
   "metadata": {},
   "source": [
    "### Check assumption of normality\n",
    "\n",
    "In the case of paired data, the assumption of the t-test is that \n",
    "the differences between conditions (for each participant) are normally distributed - let's add a column to our <tt>pandas</tt> data frame to contain the differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "27e72564",
   "metadata": {
    "tags": []
   },
   "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>cookery</th>\n",
       "      <th>horror</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60.4</td>\n",
       "      <td>72.9</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.9</td>\n",
       "      <td>57.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54.4</td>\n",
       "      <td>68.3</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.0</td>\n",
       "      <td>57.4</td>\n",
       "      <td>-2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67.7</td>\n",
       "      <td>58.7</td>\n",
       "      <td>-9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56.2</td>\n",
       "      <td>47.0</td>\n",
       "      <td>-9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>61.9</td>\n",
       "      <td>71.8</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>58.9</td>\n",
       "      <td>62.1</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>65.6</td>\n",
       "      <td>68.6</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>54.6</td>\n",
       "      <td>73.8</td>\n",
       "      <td>19.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>85.2</td>\n",
       "      <td>93.1</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>87.8</td>\n",
       "      <td>94.8</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>90.5</td>\n",
       "      <td>111.4</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>92.7</td>\n",
       "      <td>89.7</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>85.4</td>\n",
       "      <td>97.4</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>77.5</td>\n",
       "      <td>90.9</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>81.3</td>\n",
       "      <td>83.9</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>79.7</td>\n",
       "      <td>86.9</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>96.8</td>\n",
       "      <td>90.1</td>\n",
       "      <td>-6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>81.9</td>\n",
       "      <td>75.4</td>\n",
       "      <td>-6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cookery  horror  diff\n",
       "0      60.4    72.9  12.5\n",
       "1      53.9    57.0   3.1\n",
       "2      54.4    68.3  13.9\n",
       "3      60.0    57.4  -2.6\n",
       "4      67.7    58.7  -9.0\n",
       "5      56.2    47.0  -9.2\n",
       "6      61.9    71.8   9.9\n",
       "7      58.9    62.1   3.2\n",
       "8      65.6    68.6   3.0\n",
       "9      54.6    73.8  19.2\n",
       "10     85.2    93.1   7.9\n",
       "11     87.8    94.8   7.0\n",
       "12     90.5   111.4  20.9\n",
       "13     92.7    89.7  -3.0\n",
       "14     85.4    97.4  12.0\n",
       "15     77.5    90.9  13.4\n",
       "16     81.3    83.9   2.6\n",
       "17     79.7    86.9   7.2\n",
       "18     96.8    90.1  -6.7\n",
       "19     81.9    75.4  -6.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heartRates['diff'] = heartRates.horror - heartRates.cookery\n",
    "heartRates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a17cf9a6",
   "metadata": {},
   "source": [
    "Now let's plot the differences to get a sense of whether they are normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "444b9b4f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data = heartRates,  x='diff', fill=True)\n",
    "sns.rugplot(data = heartRates, x='diff', height=0.1,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8feb5101",
   "metadata": {},
   "source": [
    "The distribution looks fairly normal - for the sake of this example we can safely go ahead and use the t-test (although in real life I think it is always tricky to know if the data are really normally distributed, especially if the sample is small)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f7dcbaa",
   "metadata": {},
   "source": [
    "### Hypotheses\n",
    "\n",
    "$\\mathcal{H_o}$: the mean difference in heart rate for an individual watching cookery or horror shows is zero\n",
    "\n",
    "$\\mathcal{H_a}$: the mean difference in heart rate is positive (higher heart rate for horror)\n",
    "    \n",
    "This is a one tailed test as the researcher's hypothesis (described above) is directional - \n",
    "she thinks horror movies increase heart rate\n",
    "\n",
    "We will test at the $\\alpha = 0.05$ significance level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f87356aa",
   "metadata": {},
   "source": [
    "### Descriptive statistics\n",
    "\n",
    "First, we obtain the relevant desriptive statistics. \n",
    "By relevant, I mean the ones that go into the equation for the t-test:\n",
    "\n",
    "$$ t = \\frac{\\bar{d}}{\\frac{s_d}{\\sqrt{n}}} $$\n",
    "\n",
    "This would be the means difference in heart rate for horror-cookery $\\bar{d}$,\n",
    "the standard deviations of the differences $s_d$ and the number of participants $n$.\n",
    "\n",
    "We obtain the descriptive statistics for each column in our dataframe using the <tt>describe()</tt> method as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "edf0066e",
   "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>cookery</th>\n",
       "      <th>horror</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>72.620000</td>\n",
       "      <td>77.560000</td>\n",
       "      <td>4.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.612489</td>\n",
       "      <td>16.678047</td>\n",
       "      <td>9.049013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>53.900000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>-9.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>59.725000</td>\n",
       "      <td>66.750000</td>\n",
       "      <td>-2.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>72.600000</td>\n",
       "      <td>74.600000</td>\n",
       "      <td>5.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>85.250000</td>\n",
       "      <td>90.300000</td>\n",
       "      <td>12.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>96.800000</td>\n",
       "      <td>111.400000</td>\n",
       "      <td>20.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cookery      horror       diff\n",
       "count  20.000000   20.000000  20.000000\n",
       "mean   72.620000   77.560000   4.940000\n",
       "std    14.612489   16.678047   9.049013\n",
       "min    53.900000   47.000000  -9.200000\n",
       "25%    59.725000   66.750000  -2.700000\n",
       "50%    72.600000   74.600000   5.100000\n",
       "75%    85.250000   90.300000  12.125000\n",
       "max    96.800000  111.400000  20.900000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heartRates.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c451bdf8",
   "metadata": {},
   "source": [
    "So the mean difference is 4.94 beats per minute (higher in the horror condition). This is quite large compared to the mean heart rate in each condition (72.6 bpm in the cookery condition and 77.5 in the horror condition) - more than 5% difference. So on average the horror movie is producing quite a noticeable increase in heart rate. **Because the difference in mean heart rate is better understoon in the cotext of knowing the mean in each condition, I think it is good practice to report the condition means** (mean for cookery and horror) **even though thhey don't get used in the t-test.**\n",
    "\n",
    "The standard deviation of differences is 9.04 bpm. This is lower than the standard deviation within each condition (14.6 and 16.7 for cookery and horror), which reflects the correlation between heart rates in the two conditions, as seen in the scatter plot above.\n",
    "\n",
    "The number of participants is 20."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef4dbdfe",
   "metadata": {},
   "source": [
    "### Carry out the test\n",
    "\n",
    "We carry out a paired samples t-test using the function `stats.ttest_rel` (that's <tt>rel</tt> for related samples) from `scipy.stats`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c33aecaa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TtestResult(statistic=2.4414101572270717, pvalue=0.012293439285066588, df=19)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(heartRates.horror, heartRates.cookery, alternative='greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "745b0501",
   "metadata": {},
   "source": [
    "The inputs to stats.ttest are the two samples to be compared (columns from our Pandas data frame <tt>heartRates</tt>) \n",
    "and the argument <tt>alternative='greater'</tt>, which tells the computer to run a one tailed test \n",
    "that mean of the first input <tt>heartRate.horror</tt> is greater than the second <tt>heartRate.cookery</tt>.\n",
    "\n",
    "The outputs are <tt>statistic</tt> ($t=2.44$) and <tt>pvalue</tt> ($p=0.0122$) - if this is less than our $\\alpha$ value 0.5, there is a significant difference\n",
    "\n",
    "\n",
    "### Degrees of freedom\n",
    "\n",
    "In a scientific write-up we also need to report the degrees of freedom of the test. This tells us how many observations (data-points) the test was based on, corrected for the number of means we had to estimate from the data in order to do the test.\n",
    "\n",
    "In the case of the paired samples t-test $df = n-1$ where $n$ is the number of pairs, so in this case, df=19 and we can report out test results as:\n",
    "\n",
    "$t(19) = 2.44, p=0.0122$ (one-tailed)\n",
    "\n",
    "### Interpretation\n",
    "\n",
    "Our t value of 2.27 means that the mean increase in heart rate from the cookery to horror conditions is 2.27 times the standard error (where $ SE = \\frac{s}{\\sqrt{n}}$). \n",
    "\n",
    "Such a large difference (in the expected direction) would occur 0.0123 (1.23%) of the time due to chance if the null hypothesis were true (if the TV show made no difference to the heart rate), hence the p value of 0.0123.\n",
    "\n",
    "This diagram shows the expected distribution of t-values if the null hypothesis was true, with our obtained t-value marked:\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook/main/images/ttestHorror.png\" width=50% alt=\"There should be a picture of the t-distribution here\" >\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d41905aa",
   "metadata": {},
   "source": [
    "## Write-up \n",
    "<br>\n",
    "\n",
    "<div style = \"    padding-top: 10px;\n",
    "    padding-bottom: 10px;\n",
    "    padding-left: 10px;\n",
    "    padding-right: 10px;\n",
    "    box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);\n",
    "    vertical-align: middle;\">\n",
    "    \n",
    "Above, I walked you through how to run the t-test and why we make different choices. \n",
    "    \n",
    "In this section we practice writing up our analysis in the correct style for a scientific report. \n",
    "    \n",
    "Replace the XXXs with the correct values!    \n",
    "\n",
    "</div>\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "541151e1",
   "metadata": {},
   "source": [
    "We tested the hypothesis that heart rate increases when watching a horror show as opposed to a cookery show.\n",
    "\n",
    "For 20 participants, average heart rate was measured over 30min watching a horror show and, on a separate day, 30min watching a cookery show (repeated measures design). The order of conditions was counterbalanced.\n",
    "\n",
    "Data are shown below - there appears to be a strong effect of resting heart rate (individuals with high heart rates in one condition have high heart rates in the other condition) and heart rates are generally higher in the horror condition:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d94629f2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data = heartRates, x=\"cookery\", y=\"horror\")\n",
    "plt.xlabel('heart rate: cookery')\n",
    "plt.ylabel('heart rate: horror')\n",
    "\n",
    "# add the line x=y (ie a line from point(50,50) to (110,110)) for reference \n",
    "plt.plot([50,110],[50,110],'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90e1e7e6",
   "metadata": {},
   "source": [
    "**Note** - *the red dashed line is the line of equality $(x=y)$; heart rate is generally higher for each individual in the horror condition (most points lie above teh line $(x=y)$. There is a strong correlation between the two measures of heart rate for each individual, indicating an individual differences in heart rate regardless of condition, which should be controlled by the use of a repeated measures design.*\n",
    "\n",
    "The mean increase in heart rate in the horror condition was X.XX beats per minute (condition means were XX.X bpm for cookery and XX.X for horror). The standard deviation of differences in heart rate was X.XX bpm (condition standard deviations were XX.X bpm for cookery and XX.X for horror). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bbe31317",
   "metadata": {
    "tags": []
   },
   "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>cookery</th>\n",
       "      <th>horror</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>72.620000</td>\n",
       "      <td>77.560000</td>\n",
       "      <td>4.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.612489</td>\n",
       "      <td>16.678047</td>\n",
       "      <td>9.049013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>53.900000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>-9.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>59.725000</td>\n",
       "      <td>66.750000</td>\n",
       "      <td>-2.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>72.600000</td>\n",
       "      <td>74.600000</td>\n",
       "      <td>5.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>85.250000</td>\n",
       "      <td>90.300000</td>\n",
       "      <td>12.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>96.800000</td>\n",
       "      <td>111.400000</td>\n",
       "      <td>20.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cookery      horror       diff\n",
       "count  20.000000   20.000000  20.000000\n",
       "mean   72.620000   77.560000   4.940000\n",
       "std    14.612489   16.678047   9.049013\n",
       "min    53.900000   47.000000  -9.200000\n",
       "25%    59.725000   66.750000  -2.700000\n",
       "50%    72.600000   74.600000   5.100000\n",
       "75%    85.250000   90.300000  12.125000\n",
       "max    96.800000  111.400000  20.900000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heartRates = pd.read_csv('https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook_2024/main/data/HeartRates.csv')\n",
    "heartRates['diff'] = heartRates.horror - heartRates.cookery\n",
    "heartRates.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75c3a7a",
   "metadata": {},
   "source": [
    "The differences in heart rate were determined by inspection to be approximately normally distrbuted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "101fe433",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Density')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.kdeplot(data = heartRates,  x='diff', fill=True)\n",
    "sns.rugplot(data = heartRates, x='diff', height=0.1,)\n",
    "plot.set_xlabel(\"heart rate (BPM)\", fontsize = 16)\n",
    "plot.set_ylabel(\"Density\", fontsize = 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26026707",
   "metadata": {},
   "source": [
    "An paired samples t-test was therefore used to compare the means (alpha = XXX, XXX-tailed). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "533de1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=2.441410157227071, pvalue=0.012293439285066599)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(heartRates.horror, heartRates.cookery, alternative='greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c83db7d7",
   "metadata": {},
   "source": [
    "Heart rates were indeed significantly increased in the horror condition: t(19) = X.XX, p=X.XXX.\n",
    "\n",
    "As p<0.05 we conclude that on average, individuals' heart rates increase when watching horror shows as opposed to cookery shows."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d92fe13a",
   "metadata": {},
   "source": [
    "\n",
    "## Further Exercises\n",
    "<ol>\n",
    "<li> What do you think would happen if you ran an independent samples t-test on the data - would the p value become more or less significant? Why?\n",
    "<li> Change the code to actually run an independent samples test. Was you intuition correct?\n",
    "</ol>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}