Test+a+Perceptual+Phenomenon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the Stroop Effect\n",
    "Perform the analysis in the space below. Remember to follow [the instructions](https://docs.google.com/document/d/1-OkpZLjG_kX9J6LIQ5IltsqMzVWjh36QpnP2RYpVdPU/pub?embedded=True) and review the [project rubric](https://review.udacity.com/#!/rubrics/71/view) before submitting. Once you've completed the analysis and write up, download this file as a PDF or HTML file and submit in the next section.\n",
    "\n",
    "\n",
    "(1) What is the independent variable? What is the dependent variable?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Independent variable : congruent word condition, incongruent word condition\n",
    "\n",
    "Dependent variable : the time it takes to name the ink colors in equally-sized lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) What is an appropriate set of hypotheses for this task? What kind of statistical test do you expect to perform? Justify your choices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Null Hypothesis ( $H_{0}:\\mu_{1}=\\mu_{2}$)**\n",
    "* The average population of time it takes to name the ink colors in equally-sized lists for two groups(  congruent and incongruent) are equal.\n",
    "\n",
    "**Alternative Hypothesis ( $H_{0}:\\mu_{1}\\ne\\mu_{2}$)**\n",
    "* $H_{1}$ : The average population of time it takes to name the ink colors in equally-sized lists for two groups(  congruent and incongruent) are not equal.\n",
    "\n",
    "Statistical Test : dependent t-test (two tailed)\n",
    "Justification : \n",
    "* We need to compare the means of two related groups to determine the statistically significant difference between two means.\n",
    "* We are assuming normal distribution\n",
    "* There are less than 30 samples\n",
    "* it has to be two tailed because our hypothesis is testing equality of two means. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) Report some descriptive statistics regarding this dataset. Include at least one measure of central tendency and at least one measure of variability. The name of the data file is 'stroopdata.csv'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Congruent  Incongruent\n",
      "0      12.079       19.278\n",
      "1      16.791       18.741\n",
      "2       9.564       21.214\n",
      "3       8.630       15.687\n",
      "4      14.669       22.803\n",
      "5      12.238       20.878\n",
      "6      14.692       24.572\n",
      "7       8.987       17.394\n",
      "8       9.401       20.762\n",
      "9      14.480       26.282\n",
      "10     22.328       24.524\n",
      "11     15.298       18.644\n",
      "12     15.073       17.510\n",
      "13     16.929       20.330\n",
      "14     18.200       35.255\n",
      "15     12.130       22.158\n",
      "16     18.495       25.139\n",
      "17     10.639       20.429\n",
      "18     11.344       17.425\n",
      "19     12.369       34.288\n",
      "20     12.944       23.894\n",
      "21     14.233       17.960\n",
      "22     19.710       22.058\n",
      "23     16.004       21.157\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Congruent</th>\n",
       "      <th>Incongruent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.051125</td>\n",
       "      <td>22.015917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.559358</td>\n",
       "      <td>4.797057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.630000</td>\n",
       "      <td>15.687000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.895250</td>\n",
       "      <td>18.716750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.356500</td>\n",
       "      <td>21.017500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.200750</td>\n",
       "      <td>24.051500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.328000</td>\n",
       "      <td>35.255000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Congruent  Incongruent\n",
       "count  24.000000    24.000000\n",
       "mean   14.051125    22.015917\n",
       "std     3.559358     4.797057\n",
       "min     8.630000    15.687000\n",
       "25%    11.895250    18.716750\n",
       "50%    14.356500    21.017500\n",
       "75%    16.200750    24.051500\n",
       "max    22.328000    35.255000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# code adapted from http://pandas.pydata.org/pandas-docs/stable\n",
    "import pandas as pd\n",
    "\n",
    "# read csv file and save to df\n",
    "df = pd.read_csv('stroopdata.csv')\n",
    "\n",
    "# print csv data\n",
    "print(df)\n",
    "\n",
    "# Describe shows a quick statistic summary of your data\n",
    "df.describe()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** central tendency **\n",
    "* congruent mean = 14.0511\n",
    "* incongruent mean = 22.0159\n",
    "\n",
    "** measure of variability **\n",
    "* congruent std = 3.5594\n",
    "* incongruent std = 4.7971"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) Provide one or two visualizations that show the distribution of the sample data. Write one or two sentences noting what you observe about the plot or plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1724ae6be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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doAuq8qD4mFpIEgLeXgoFGrgKf/+KamCw8LeW7R99sZrN29oG4PBKlWpmrlq1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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f170145a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# code adapted from http://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('stroopdata.csv')\n",
    "plt.figure(); df.plot();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff965b0fdd8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7BfT64XtnKi7Jqu55X1pVa+gNLP642/xU4FtdjTcAb6uq/wC+Ary/66/7bZiD1xRdbGuB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\nS1Ij/g9jIOHIEFFqRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff989b91390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f17013cf208>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AkgXgeOACNsC+t1fepaay781E4Y/n3p4E3Bv4ReCwJE+fbqrbtiQvZ/Sn51nTzjJJks3A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17014243c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Congruent'].plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1700fb3780>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ng4ZLAacBFw3LJ21rTwLeVlV3VNWngesZjWXT2qrq0uG+Aq5gPtvbpHGbRbPX6LLNNof/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\nYeBLUif+DxsrYbqEo68eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17013a11d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Incongruent'].plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16f2f59630>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1700f66dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at the boxplot, we can see that the average completion time of Incongruent group is higher. The box plot also shows that the incongruent group has two outliers. The distribution for both congruent and incongruent group looks normal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(5) Now, perform the statistical test and report the results. What is the confidence level and your critical statistic value? Do you reject the null hypothesis or fail to reject it? Come to a conclusion in terms of the experiment task. Did the results match up with your expectations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-8.020706944109957, pvalue=4.1030005857111781e-08)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform the statistical test here\n",
    "# https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html\n",
    "from scipy import stats\n",
    "degreesOfFreedom = 23\n",
    "stats.t.ppf(0.975, degreesOfFreedom)\n",
    "\n",
    "# Calculates the T-test on TWO RELATED samples of scores, a and b.\n",
    "stats.ttest_rel(df['Congruent'], df['Incongruent'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "p value is under 0.05 therefore, we reject our null hypothesis. Thus, we can conclude that incongruent group and congruent group has different average population time to complete tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference\n",
    "* [Latex Symbols](http://reu.dimacs.rutgers.edu/Symbols.pdf)\n",
    "* [Dependent T-Test](https://statistics.laerd.com/statistical-guides/dependent-t-test-statistical-guide-2.php)\n",
    "* [Determining Sample Size](https://www.qualtrics.com/blog/determining-sample-size/)\n",
    "* [About the null and alternative hypothesis](http://support.minitab.com/en-us/minitab/17/topic-library/basic-statistics-and-graphs/hypothesis-tests/basics/null-and-alternative-hypotheses/)\n",
    "* [Which chart or graph is right for you?](https://www.tableau.com/sites/default/files/media/which_chart_v6_final_0.pdf)\n",
    "* [pandas visualization](http://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization)\n",
    "* [scipy stats](https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}