{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5cfdceec",
   "metadata": {},
   "source": [
    "# Independent 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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38124844",
   "metadata": {},
   "source": [
    "## Example\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook/main/images/appletree.jpg\" width=30% >\n",
    "\n",
    "\n",
    "Below we have data on the average weight, in grams, of apples from each of 20 apple trees.\n",
    "\n",
    "10 of the trees received MiracleGro fertilizer and the other 10 received Brand X.\n",
    "\n",
    "Test the hypothesis that the trees given MiracleGro produced heavier apples."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ba6e37",
   "metadata": {},
   "source": [
    "### Inspect the data\n",
    "\n",
    "The data are provided in a text (.csv) file.\n",
    "\n",
    "Let's load the data as a Pandas dataframe, and plot them to get a sense for their distribution (is it normal?) and any outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3f184f9d",
   "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>Fertilizer</th>\n",
       "      <th>meanAppleWeight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fertilizer  meanAppleWeight\n",
       "0       BrandX              172\n",
       "1       BrandX              165\n",
       "2       BrandX              175\n",
       "3       BrandX              164\n",
       "4       BrandX              165\n",
       "5       BrandX              157\n",
       "6       BrandX              183\n",
       "7       BrandX              186\n",
       "8       BrandX              191\n",
       "9       BrandX              173\n",
       "10  MiracleGro              164\n",
       "11  MiracleGro              198\n",
       "12  MiracleGro              184\n",
       "13  MiracleGro              200\n",
       "14  MiracleGro              180\n",
       "15  MiracleGro              189\n",
       "16  MiracleGro              177\n",
       "17  MiracleGro              170\n",
       "18  MiracleGro              192\n",
       "19  MiracleGro              193\n",
       "20  MiracleGro              187\n",
       "21  MiracleGro              176"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the data and have a look\n",
    "apples = pd.read_csv('https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook_2024/main/data/AppleWeights.csv')\n",
    "apples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce8aef6",
   "metadata": {},
   "source": [
    "Let's plot the data and see if they look Normally distributed.\n",
    "\n",
    "As we saw in the session on plotting, a good choice here will be a KDE plot (to get an estimate of the shape of the distribution) and a rug plot (individual data values as the KDE plot is based on only a small sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b4ca4f7",
   "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=apples, x='meanAppleWeight', hue='Fertilizer', fill=True)\n",
    "sns.rugplot(data=apples, x='meanAppleWeight',  hue='Fertilizer', height=0.1)\n",
    "plt.xlabel(\"Mean weight of apples (g)\", fontsize = 12)\n",
    "plt.ylabel(\"Density\", fontsize = 12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08178c3d",
   "metadata": {},
   "source": [
    "It looks like both distributions are unimodal but a bit skewed. \n",
    "\n",
    "Typically, it would be hard to say if the data are really non-normal due to the small number \n",
    "of data points (the apparent skew could be a fluke). By default, I would err on the side of caution and use a non-parametric test.\n",
    "\n",
    "However, in this particular case I am confident that the data in each sample are drawn from a normal distribution \n",
    "because each data point represents the mean of a large sample (the weights of all apples on one tree) \n",
    "and such means are always normally distributed due to the Central Limit theorem.\n",
    "There is a video explaining why here.\n",
    "\n",
    "So we can go ahead and use a t-test."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b80d53",
   "metadata": {},
   "source": [
    "### Hypotheses\n",
    "\n",
    "$\\mathcal{H_o}$: the mean weight of apples is the same for trees fertilized with MiracleGro and Brand X\n",
    "\n",
    "$\\mathcal{H_a}$: the mean weight of apples is greater for trees fertilized with MiracleGro\n",
    "    \n",
    "This is a one tailed test as the manufacturers of MiracleGro are only looking for an effect in one direction \n",
    "(evidence that MiracleGro is better!)\n",
    "\n",
    "We will test at the $\\alpha = 0.05$ significance level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79fc39f",
   "metadata": {},
   "source": [
    "### Descriptive statistics\n",
    "\n",
    "First, we obtain the relevant desriptive statistics. By relevant, I mean the ones that go into the equation for the t-test:\n",
    "\n",
    "\n",
    "$$ t = \\frac{\\bar{x_1} - \\bar{x_2}}{s \\sqrt{\\frac{1}{n_1}+\\frac{1}{n_2}}} $$\n",
    "\n",
    "This would be the sample means $\\bar{x_1}$ and $\\bar{x_2}$, and the standard deviations for both samples (these feed into the pooled standard deviation $s$) and the sample size $n$.\n",
    "\n",
    "Remember, <tt>apples</tt> is our original Pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4825c72f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fertilizer</th>\n",
       "      <th>meanAppleWeight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>172</td>\n",
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       "      <td>165</td>\n",
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       "      <td>175</td>\n",
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       "      <th>3</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>164</td>\n",
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       "      <th>4</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>165</td>\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
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       "      <th>7</th>\n",
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       "      <td>186</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>191</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BrandX</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MiracleGro</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fertilizer  meanAppleWeight\n",
       "0       BrandX              172\n",
       "1       BrandX              165\n",
       "2       BrandX              175\n",
       "3       BrandX              164\n",
       "4       BrandX              165\n",
       "5       BrandX              157\n",
       "6       BrandX              183\n",
       "7       BrandX              186\n",
       "8       BrandX              191\n",
       "9       BrandX              173\n",
       "10  MiracleGro              164\n",
       "11  MiracleGro              198\n",
       "12  MiracleGro              184\n",
       "13  MiracleGro              200\n",
       "14  MiracleGro              180\n",
       "15  MiracleGro              189\n",
       "16  MiracleGro              177\n",
       "17  MiracleGro              170\n",
       "18  MiracleGro              192\n",
       "19  MiracleGro              193\n",
       "20  MiracleGro              187\n",
       "21  MiracleGro              176"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(apples)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f37ee4a",
   "metadata": {},
   "source": [
    "We obtain some commonly used descriptive statistics using the `df.describe()` method in `pandas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3832fb73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meanAppleWeight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>179.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.123552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>157.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>170.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>178.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>188.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       meanAppleWeight\n",
       "count        22.000000\n",
       "mean        179.136364\n",
       "std          12.123552\n",
       "min         157.000000\n",
       "25%         170.500000\n",
       "50%         178.500000\n",
       "75%         188.500000\n",
       "max         200.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apples.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09028ae0",
   "metadata": {},
   "source": [
    "We need the descriptive statistics separately for each fertilizer type. \n",
    "\n",
    "We could use the separate dataframes that we created for plotting, but `pandas` has a handy method called `df.groupby()` that will do the job:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3b0c2aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">meanAppleWeight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fertilizer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BrandX</th>\n",
       "      <td>10.0</td>\n",
       "      <td>173.100000</td>\n",
       "      <td>10.867382</td>\n",
       "      <td>157.0</td>\n",
       "      <td>165.00</td>\n",
       "      <td>172.5</td>\n",
       "      <td>181.00</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiracleGro</th>\n",
       "      <td>12.0</td>\n",
       "      <td>184.166667</td>\n",
       "      <td>11.101460</td>\n",
       "      <td>164.0</td>\n",
       "      <td>176.75</td>\n",
       "      <td>185.5</td>\n",
       "      <td>192.25</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           meanAppleWeight                                               \\\n",
       "                     count        mean        std    min     25%    50%   \n",
       "Fertilizer                                                                \n",
       "BrandX                10.0  173.100000  10.867382  157.0  165.00  172.5   \n",
       "MiracleGro            12.0  184.166667  11.101460  164.0  176.75  185.5   \n",
       "\n",
       "                           \n",
       "               75%    max  \n",
       "Fertilizer                 \n",
       "BrandX      181.00  191.0  \n",
       "MiracleGro  192.25  200.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apples.groupby([\"Fertilizer\"]).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c672fd",
   "metadata": {},
   "source": [
    "It does look like the mean weight of apples from the MiracleGro trees is higher, but is the difference statistically significant?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255f692a",
   "metadata": {},
   "source": [
    "### Carry out the test\n",
    "\n",
    "We carry out an independent samples t-test using the function <tt>ttest_ind</tt> from <tt>scipy.stats</tt>, here loaded as <tt>stats</tt>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "744b1ab0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=2.350347501385599, pvalue=0.014564862730138283)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(apples.query('Fertilizer==\"MiracleGro\"').meanAppleWeight, \n",
    "                apples.query('Fertilizer==\"BrandX\"').meanAppleWeight,\n",
    "                alternative='greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dd4feae",
   "metadata": {},
   "source": [
    "The inputs to stats.ttest are the two samples to be compared (the values in the <tt>meanAppleWeight</tt> column from our separated Pandas data frames <tt>apples_BrandX</tt> and <tt>apples_MiracleGro</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>apples.MiracleGro</tt> is greater than the second <tt>apples.BrandX</tt>.\n",
    "\n",
    "The outputs are <tt>statistic</tt> ($t=2.35$) and <tt>pvalue</tt> ($p=0.0146$) - if this is less than our $\\alpha$ value 0.5, there is a significant difference.\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 independent samples t-test $df = n_1 + n_2 - 2$ so in this case, df=(10+12-2)=20 and we can report out test results as:\n",
    "\n",
    "$t(20) = 2.35, p=0.0146$ (one-tailed)\n",
    "\n",
    "### Interpretation\n",
    "\n",
    "Our t value of 2.35 means that the difference in mean apple weights between MiracleGro and BrandX trees is 2.35 times the standard error (where $ SE = s \\sqrt{\\frac{1}{n_1}+\\frac{1}{n_2}}$).\n",
    "\n",
    "Such a large difference (in the expected direction) would occur 0.0146 (1.46%) of the time due to chance if the null hypothesis were true (if MiracleGro was really no better than Brand X), hence the p value of 0.0146.\n",
    "\n",
    "This diagram shows the expected distribution of t-values if the null were true, with our obtained t-value marked:\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook/main/images/ttestApples.png\" width=50% alt=\"There should be a picture of the t-distribution here\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f263742",
   "metadata": {},
   "source": [
    "### Draw conclusions\n",
    "\n",
    "As p<0.05 we conclude that the mean weight of apples on trees fertilized with MiracleGro is indeed greater than the mean weight of apples on trees fertilized with Brand X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f1c539",
   "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 revisit the analysis, but here 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": "6f5439f3",
   "metadata": {},
   "source": [
    "    \n",
    "We tested the hypothesis that the mean weight of apples produced by trees fertilized with MiracleGro was higher than for trees fertilized with Brand X.\n",
    "\n",
    "The mean apple weight was measured for each of XX trees fertilized with MiracleGro (mean of mean apple weights over XX trees XXX.Xg, sd of mean apple weights over 100 trees, XXX.Xg) and XX trees fertilized with Brand X (mean over XX trees XXX.Xg, sd XXX.Xg).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5403735d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">meanAppleWeight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fertilizer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BrandX</th>\n",
       "      <td>10.0</td>\n",
       "      <td>173.100000</td>\n",
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       "      <td>157.0</td>\n",
       "      <td>165.00</td>\n",
       "      <td>172.5</td>\n",
       "      <td>181.00</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiracleGro</th>\n",
       "      <td>12.0</td>\n",
       "      <td>184.166667</td>\n",
       "      <td>11.101460</td>\n",
       "      <td>164.0</td>\n",
       "      <td>176.75</td>\n",
       "      <td>185.5</td>\n",
       "      <td>192.25</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           meanAppleWeight                                               \\\n",
       "                     count        mean        std    min     25%    50%   \n",
       "Fertilizer                                                                \n",
       "BrandX                10.0  173.100000  10.867382  157.0  165.00  172.5   \n",
       "MiracleGro            12.0  184.166667  11.101460  164.0  176.75  185.5   \n",
       "\n",
       "                           \n",
       "               75%    max  \n",
       "Fertilizer                 \n",
       "BrandX      181.00  191.0  \n",
       "MiracleGro  192.25  200.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apples = pd.read_csv('https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook_2024/main/data/AppleWeights.csv')\n",
    "apples.groupby([\"Fertilizer\"]).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59fd722c",
   "metadata": {},
   "source": [
    "Theoretical considerations suggest that data for each group of trees should be drawn from a normal distribution: as individual data points were themselves the means of large samples (all apples from a given tree), these data points should follow a normal distribubtion due to the Central Limit Theorem. This was supported by a plot of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e06d0871",
   "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=apples, x='meanAppleWeight', hue='Fertilizer', fill=True)\n",
    "sns.rugplot(data=apples, x='meanAppleWeight',  hue='Fertilizer', height=0.1)\n",
    "plt.xlabel(\"Mean weight of apples (g)\", fontsize = 12)\n",
    "plt.ylabel(\"Density\", fontsize = 12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6550a8cb",
   "metadata": {},
   "source": [
    "An independent samples t-test was therefore used to compare the means (alpha = XXX, XXX-tailed). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "36f701e7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=2.350347501385599, pvalue=0.014564862730138283)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(apples.query('Fertilizer==\"MiracleGro\"').meanAppleWeight, \n",
    "                apples.query('Fertilizer==\"BrandX\"').meanAppleWeight,\n",
    "                alternative='greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5d4dc7",
   "metadata": {},
   "source": [
    "The weight of apples from the trees fertilized with MiracleGro was indeed found to be significantly higher: t(18) = 2.35, p=0.0146."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a113d25",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "1. Can you work out how to run a two-tailed test on the data?\n",
    "* Info on the possible values for 'alternative' can be found on the\n",
    "<a href=\"https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html\"> scipy reference page </a>\n",
    "\n",
    "2. What happens to the p value if you run a two tailed test instead of one tailed? Is it more or less significant? Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27c4d091-033f-4a3e-bee7-b464b261487d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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