Test a Perceptual Phenomenon.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "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:** Type of word(Congruent or Incongruent)\n",
    "\n",
    "**Dependent variable:** Time taken to name the color of the incongruent words(speed)."
   ]
  },
  {
   "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:** The reading speed for incongruent words is same.\n",
    "\n",
    "**ALTERNATE HYPOTHESIS:** The reading speed for incongruent words is different.\n",
    "\n",
    "$$ H_0: \\mu _i = \\mu _c$$\n",
    "$$ H_1: \\mu _i \\neq \\mu _c$$\n",
    "\n",
    "Here $H_{0}$ and $H_{1}$ are the null and the alternative hypothesis respectively.\n",
    "\n",
    "$\\mu_{i}$ is the mean time for incongruent words and $\\mu_{c}$ is the mean time for congruent words.\n",
    "\n",
    "We will perform a t-test to see whether the difference in time for congruent and incongruent words is significantly different from zero.\n",
    "\n",
    "We are using paired t-test because the group of participants are same and they are tested under two different cases. The t-statistic will also help in accepting or rejecting the alternative hypothesis.\n",
    "\n",
    "**Assumption/Reasoning:** We are using t-test instead of z-test to compare the mean of two populations here because standard deviation of the populations is not known and the sample size is less than 30(here it is 24).\n",
    "\n",
    "Another assumption to use t-test is that we consider the population to be a normal distribution."
   ]
  },
  {
   "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from scipy.stats import t\n",
    "\n",
    "df = pd.read_csv(\"stroopdata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Congruent</th>\n",
       "      <th>Incongruent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.079</td>\n",
       "      <td>19.278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.791</td>\n",
       "      <td>18.741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.564</td>\n",
       "      <td>21.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.630</td>\n",
       "      <td>15.687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.669</td>\n",
       "      <td>22.803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.238</td>\n",
       "      <td>20.878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.692</td>\n",
       "      <td>24.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.987</td>\n",
       "      <td>17.394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.401</td>\n",
       "      <td>20.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.480</td>\n",
       "      <td>26.282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22.328</td>\n",
       "      <td>24.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15.298</td>\n",
       "      <td>18.644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.073</td>\n",
       "      <td>17.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16.929</td>\n",
       "      <td>20.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18.200</td>\n",
       "      <td>35.255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12.130</td>\n",
       "      <td>22.158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18.495</td>\n",
       "      <td>25.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.639</td>\n",
       "      <td>20.429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.344</td>\n",
       "      <td>17.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12.369</td>\n",
       "      <td>34.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12.944</td>\n",
       "      <td>23.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14.233</td>\n",
       "      <td>17.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19.710</td>\n",
       "      <td>22.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16.004</td>\n",
       "      <td>21.157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    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"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "<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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample size = 24\n",
    "\n",
    "Mean time for congruent words: 14.05\n",
    "\n",
    "Mean time for incongruent words: 22.02\n",
    "\n",
    "Standard deviation for this sample:\n",
    "\n",
    "- congruent: 3.56\n",
    "\n",
    "- incongruent: 4.80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24.000000\n",
       "mean      7.964792\n",
       "std       4.864827\n",
       "min       1.950000\n",
       "25%       3.645500\n",
       "50%       7.666500\n",
       "75%      10.258500\n",
       "max      21.919000\n",
       "Name: Difference, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Difference'] = abs(df.Congruent - df.Incongruent)\n",
    "df.Difference.describe()"
   ]
  },
  {
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5178cb588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplots of two condition and difference\n",
    "df.plot(title = \"Box plots for both type of words and their differences\", kind = \"box\")\n",
    "y = plt.ylabel(\"Time (in seconds)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb51782f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of congruent condition\n",
    "sns.distplot(df['Congruent'], bins = 8);\n",
    "plt.xlabel(\"Time (in seconds)\");\n",
    "plt.axis([0,40,0,0.15]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5177a7b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of incongruent condition\n",
    "sns.distplot(df['Incongruent'], bins = 8);\n",
    "plt.xlabel(\"Time (in seconds)\");\n",
    "plt.axis([0,40,0,0.15]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5178fdd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Difference'], bins = 8);\n",
    "plt.title(\"Distribution of difference\");\n",
    "plt.xlabel(\"Time (in seconds)\");\n",
    "plt.axis([0,40,0,0.15]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the above distributions are right skewed. Especially some observations in congruent condition which took long time."
   ]
  },
  {
   "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?"
   ]
  },
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    "**mean difference** d_bar = 7.96\n",
    "\n",
    "**standard deviation** sd = 4.86\n",
    "\n",
    "**standard error of the difference** SE(d_bar) = sd/square_root(n) = 4.86/(24^0.5) = 0.99\n",
    "\n",
    "**t-statistic** = d_bar/SE(d_bar) = 7.96/0.99 = 8.04 on 23 degree of freedom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "t-statistic is 8.04"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our alternative hypothesis is that difference in time between two types of words is different from zero. For this, we have used paired t-test.\n",
    "\n",
    "We have taken alpha = 0.05 to decide statistical significance of the alternative.\n",
    "\n",
    "Since our t-statistic (8.04) is greater than the critical value (1.71), we can reject the null hypothesis. This means that reading speed for different condition is different. This is what was expected. \n",
    "\n",
    "I learned the t-score formula from [here](http://www.statisticshowto.com/t-score-formula/). "
   ]
  }
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