hedging-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Analysis for ReproHum2021 Experiment\n",
    "\n",
    "This notebook contains the analysis work performed for the reproduction experiment comparing\n",
    "hedged and non-hedged NLG texts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import pingouin as pg"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "   ID       start_time  completion_time gender              age  \\\n0   1  8/2/21 14:50:41  8/2/21 14:54:07   Male  25-34 years old   \n1   2  8/2/21 14:49:28  8/2/21 14:58:59   Male  25-34 years old   \n2   3  8/2/21 18:13:14  8/2/21 18:26:54   Male  25-34 years old   \n3   4  8/4/21 12:11:04  8/4/21 12:24:19   Male  25-34 years old   \n4   5   8/5/21 7:45:50   8/5/21 8:03:16   Male  35-44 years old   \n\n  english_language_proficiency sc1_opinion  sc1_rating  \\\n0       Non-native, but fluent       Maybe          -2   \n1       Non-native, but fluent         Yes          -2   \n2       Non-native, but fluent         Yes           0   \n3       Non-native, but fluent         Yes          -2   \n4                       Native          No          -3   \n\n                                             sc1_why sc2_opinion  sc2_rating  \\\n0                                 A bit more formal.       Maybe          -2   \n1  A is more direct. Don't think the adverbs in B...          No          -1   \n2  The text doesn't tell you anything that the ta...       Maybe          -1   \n3  The outcome is positive, so it can be conveyed...          No           2   \n4  The second one has a kind of conflict between ...          No          -3   \n\n                                             sc2_why  \n0                                        More formal  \n1  Again, do not think that the adverbs add value...  \n2  \"Average results in X. This result is extremel...  \n3  The outcome is negative, so there can be certa...  \n4  B sounds disingenuous. The degree qualificatio...  ",
      "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>ID</th>\n      <th>start_time</th>\n      <th>completion_time</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>english_language_proficiency</th>\n      <th>sc1_opinion</th>\n      <th>sc1_rating</th>\n      <th>sc1_why</th>\n      <th>sc2_opinion</th>\n      <th>sc2_rating</th>\n      <th>sc2_why</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>8/2/21 14:50:41</td>\n      <td>8/2/21 14:54:07</td>\n      <td>Male</td>\n      <td>25-34 years old</td>\n      <td>Non-native, but fluent</td>\n      <td>Maybe</td>\n      <td>-2</td>\n      <td>A bit more formal.</td>\n      <td>Maybe</td>\n      <td>-2</td>\n      <td>More formal</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>8/2/21 14:49:28</td>\n      <td>8/2/21 14:58:59</td>\n      <td>Male</td>\n      <td>25-34 years old</td>\n      <td>Non-native, but fluent</td>\n      <td>Yes</td>\n      <td>-2</td>\n      <td>A is more direct. Don't think the adverbs in B...</td>\n      <td>No</td>\n      <td>-1</td>\n      <td>Again, do not think that the adverbs add value...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>8/2/21 18:13:14</td>\n      <td>8/2/21 18:26:54</td>\n      <td>Male</td>\n      <td>25-34 years old</td>\n      <td>Non-native, but fluent</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>The text doesn't tell you anything that the ta...</td>\n      <td>Maybe</td>\n      <td>-1</td>\n      <td>\"Average results in X. This result is extremel...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>8/4/21 12:11:04</td>\n      <td>8/4/21 12:24:19</td>\n      <td>Male</td>\n      <td>25-34 years old</td>\n      <td>Non-native, but fluent</td>\n      <td>Yes</td>\n      <td>-2</td>\n      <td>The outcome is positive, so it can be conveyed...</td>\n      <td>No</td>\n      <td>2</td>\n      <td>The outcome is negative, so there can be certa...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>8/5/21 7:45:50</td>\n      <td>8/5/21 8:03:16</td>\n      <td>Male</td>\n      <td>35-44 years old</td>\n      <td>Native</td>\n      <td>No</td>\n      <td>-3</td>\n      <td>The second one has a kind of conflict between ...</td>\n      <td>No</td>\n      <td>-3</td>\n      <td>B sounds disingenuous. The degree qualificatio...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_df = pd.read_csv(\"data/hedging_dataset.csv\", sep=\";\")\n",
    "dataset_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "count    11.000000\nmean     -2.090909\nstd       1.044466\nmin      -3.000000\n25%      -3.000000\n50%      -2.000000\n75%      -1.500000\nmax       0.000000\nName: sc1_rating, dtype: float64"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe Likert ratings for scenario 1:\n",
    "dataset_df['sc1_rating'].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "count    11.000000\nmean     -1.363636\nstd       1.858641\nmin      -3.000000\n25%      -3.000000\n50%      -2.000000\n75%      -1.000000\nmax       2.000000\nName: sc2_rating, dtype: float64"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe Likert ratings for scenario 2:\n",
    "dataset_df['sc2_rating'].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Participant Analysis"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:ylabel='gender'>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gender:\n",
    "dataset_df['gender'].value_counts().plot(kind='pie')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age\n",
    "dataset_df['age'].value_counts().plot(kind='bar')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Language proficiency:\n",
    "dataset_df['english_language_proficiency'].value_counts().plot(kind='bar')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Statistical Analysis"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenario 1 Overall Mean Average Score: -2.090909090909091 (1.044465935734187 std.)\n"
     ]
    }
   ],
   "source": [
    "# Scenario 1:\n",
    "sc1_mean = dataset_df['sc1_rating'].mean()\n",
    "sc1_std = dataset_df['sc1_rating'].std()\n",
    "\n",
    "print(f\"Scenario 1 Overall Mean Average Score: {sc1_mean} ({sc1_std} std.)\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenario 2 Overall Mean Average Score: -1.3636363636363635 (1.85864075456917 std.)\n"
     ]
    }
   ],
   "source": [
    "# Scenario 2:\n",
    "sc2_mean = dataset_df['sc2_rating'].mean()\n",
    "sc2_std = dataset_df['sc2_rating'].std()\n",
    "\n",
    "print(f\"Scenario 2 Overall Mean Average Score: {sc2_mean} ({sc2_std} std.)\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## By Language Proficiency"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "                             sc1_rating                                      \\\n                                  count mean       std  min   25%  50%  75%   \nenglish_language_proficiency                                                  \nNative                              5.0 -2.2  1.095445 -3.0 -3.00 -3.0 -1.0   \nNon-native, but fluent              6.0 -2.0  1.095445 -3.0 -2.75 -2.0 -2.0   \n\n                                  sc2_rating                                 \\\n                              max      count      mean       std  min   25%   \nenglish_language_proficiency                                                  \nNative                       -1.0        5.0 -1.400000  2.073644 -3.0 -3.00   \nNon-native, but fluent        0.0        6.0 -1.333333  1.861899 -3.0 -2.75   \n\n                                             \n                              50%  75%  max  \nenglish_language_proficiency                 \nNative                       -2.0 -1.0  2.0  \nNon-native, but fluent       -1.5 -1.0  2.0  ",
      "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"8\" halign=\"left\">sc1_rating</th>\n      <th colspan=\"8\" halign=\"left\">sc2_rating</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      <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>english_language_proficiency</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      <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>Native</th>\n      <td>5.0</td>\n      <td>-2.2</td>\n      <td>1.095445</td>\n      <td>-3.0</td>\n      <td>-3.00</td>\n      <td>-3.0</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n      <td>5.0</td>\n      <td>-1.400000</td>\n      <td>2.073644</td>\n      <td>-3.0</td>\n      <td>-3.00</td>\n      <td>-2.0</td>\n      <td>-1.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>Non-native, but fluent</th>\n      <td>6.0</td>\n      <td>-2.0</td>\n      <td>1.095445</td>\n      <td>-3.0</td>\n      <td>-2.75</td>\n      <td>-2.0</td>\n      <td>-2.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>-1.333333</td>\n      <td>1.861899</td>\n      <td>-3.0</td>\n      <td>-2.75</td>\n      <td>-1.5</td>\n      <td>-1.0</td>\n      <td>2.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "dataset_df.drop(['ID'], axis=1).groupby('english_language_proficiency').describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "# Native English:\n",
    "native_english_df = dataset_df.loc[dataset_df['english_language_proficiency'] == \"Native\"]\n",
    "# Non-Native, Fluent English:\n",
    "fluent_english_df = dataset_df.loc[dataset_df['english_language_proficiency'] == 'Non-native, but fluent']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenario 1 Native Mean Average Score: -2.2 (1.0954451150103321 std.)\n"
     ]
    }
   ],
   "source": [
    "# Scenario 1 (Native):\n",
    "sc1_mean = native_english_df['sc1_rating'].mean()\n",
    "sc1_std = native_english_df['sc1_rating'].std()\n",
    "\n",
    "print(f\"Scenario 1 Native Mean Average Score: {sc1_mean} ({sc1_std} std.)\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenario 2 Native Mean Average Score: -1.4 (2.073644135332772 std.)\n"
     ]
    }
   ],
   "source": [
    "# Scenario 2 (Native):\n",
    "sc2_mean = native_english_df['sc2_rating'].mean()\n",
    "sc2_std = native_english_df['sc2_rating'].std()\n",
    "\n",
    "print(f\"Scenario 2 Native Mean Average Score: {sc2_mean} ({sc2_std} std.)\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenario 1 Fluent Mean Average Score: -2.0 (1.0954451150103321 std.)\n"
     ]
    }
   ],
   "source": [
    "# Scenario 1 (Fluent):\n",
    "sc1_mean = fluent_english_df['sc1_rating'].mean()\n",
    "sc1_std = fluent_english_df['sc1_rating'].std()\n",
    "\n",
    "print(f\"Scenario 1 Fluent Mean Average Score: {sc1_mean} ({sc1_std} std.)\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenario 2 Fluent Mean Average Score: -1.3333333333333333 (1.8618986725025255 std.)\n"
     ]
    }
   ],
   "source": [
    "# Scenario 2:\n",
    "sc2_mean = fluent_english_df['sc2_rating'].mean()\n",
    "sc2_std = fluent_english_df['sc2_rating'].std()\n",
    "\n",
    "print(f\"Scenario 2 Fluent Mean Average Score: {sc2_mean} ({sc2_std} std.)\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Data Normality and Homogeneity Checks"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Native Speakers:"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "ShapiroResult(statistic=0.6840287446975708, pvalue=0.006469968240708113)"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for Data Normality using the Shapiro-Wilks test:\n",
    "stats.shapiro(native_english_df['sc1_rating'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "ShapiroResult(statistic=0.8421509861946106, pvalue=0.17095594108104706)"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(native_english_df['sc2_rating'])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Fluent Speakers:"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "ShapiroResult(statistic=0.8137412071228027, pvalue=0.07784329354763031)"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(fluent_english_df['sc1_rating'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "ShapiroResult(statistic=0.8616126775741577, pvalue=0.19476796686649323)"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(fluent_english_df['sc2_rating'])\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Homogeneity Check (Scenario 1):"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "LeveneResult(statistic=0.05365126676602093, pvalue=0.8220079481989209)"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.levene(native_english_df['sc1_rating'], fluent_english_df['sc1_rating'])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Homogeneity Check (Scenario 2):"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "LeveneResult(statistic=0.0068040068040068, pvalue=0.9360653412936365)"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.levene(native_english_df['sc2_rating'], fluent_english_df['sc2_rating'])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Two-Sample T-test (Scenario 1):"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "               T  dof       tail     p-val        CI95%   cohen-d   BF10  \\\nT-test -0.301511    9  two-sided  0.769875  [-1.7, 1.3]  0.182574  0.495   \n\n           power  \nT-test  0.058442  ",
      "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>T</th>\n      <th>dof</th>\n      <th>tail</th>\n      <th>p-val</th>\n      <th>CI95%</th>\n      <th>cohen-d</th>\n      <th>BF10</th>\n      <th>power</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>T-test</th>\n      <td>-0.301511</td>\n      <td>9</td>\n      <td>two-sided</td>\n      <td>0.769875</td>\n      <td>[-1.7, 1.3]</td>\n      <td>0.182574</td>\n      <td>0.495</td>\n      <td>0.058442</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.ttest(native_english_df['sc1_rating'],\n",
    "                fluent_english_df['sc1_rating'], correction=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Two-Sample T-test (Scenario 2):"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "               T  dof       tail     p-val          CI95%   cohen-d   BF10  \\\nT-test -0.056205    9  two-sided  0.956406  [-2.75, 2.62]  0.034034  0.481   \n\n           power  \nT-test  0.050292  ",
      "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>T</th>\n      <th>dof</th>\n      <th>tail</th>\n      <th>p-val</th>\n      <th>CI95%</th>\n      <th>cohen-d</th>\n      <th>BF10</th>\n      <th>power</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>T-test</th>\n      <td>-0.056205</td>\n      <td>9</td>\n      <td>two-sided</td>\n      <td>0.956406</td>\n      <td>[-2.75, 2.62]</td>\n      <td>0.034034</td>\n      <td>0.481</td>\n      <td>0.050292</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.ttest(native_english_df['sc2_rating'],\n",
    "                fluent_english_df['sc2_rating'], correction=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}