{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Getting the (wrong) picture from the data\n",
"\n",
"Gabriele Degola\n",
"\n",
"Scientific Methodology and Experimental Evaluation - 2021/22"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Kindergarten"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load data:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data = [[1.36, 0.24, 0.22, 0.11, 1.25, 0.24, 0.35, 0.24, 1.6, 0.63, 0.68, 0.0, 1.44, 1.86, 3.47, 0.58, 0.0, 0.82, 2.75, 3.14, 1.58, 2.83, 1.92, 0.83, 2.47, 1.13, 1.72, 0.61, 0.35, 1.11, 1.24, 2.36, 0.5],\n",
" [2.07, 3.6, 2.57, 4.29, 5.67, 1.67, 2.43, 4.72, 7.67, 3.83, 5.13, 2.31, 1.0, 3.74, 6.64, 3.39, 2.36, 6.75, 5.13, 8.0, 7.08, 6.01, 6.25, 2.61, 3.61, 2.67, 5.24, 1.56, 1.89, 4.06, 3.11, 4.0, 2.89],\n",
" [7.63, 5.79, 7.38, 8.5, 5.75, 4.83, 5.38, 7.75, 7.54, 8.38, 7.5, 6.17, 5.68, 7.25, 7.54, 5.64, 6.46, 7.5, 8.75, 9.0, 7.25, 7.42, 6.58, 8.25, 7.46, 7.21, 7.5, 8.17, 6.96, 8.0, 6.92, 5.71, 7.0],\n",
" [1.57, 1.36, 1.25, 2.81, 0.86, 0.47, 0.35, 1.81, 1.63, 1.36, 0.35, 1.75, 0.35, 2.58, 1.58, 1.67, 0.6, 1.63, 1.06, 0.0, 2.17, 0.25, 0.0, 2.67, 2.33, 1.13, 1.22, 1.33, 1.0, 0.83],\n",
" [2.26, 3.68, 6.26, 3.88, 6.54, 5.25, 6.0, 6.67, 4.96, 2.57, 1.64, 5.83, 6.42, 6.88, 4.46, 1.94, 5.5, 1.71, 3.33, 2.28, 5.67, 5.32, 1.56, 2.32, 4.17, 6.18, 4.32, 6.5, 3.0, 2.67],\n",
" [4.42, 6.63, 6.88, 7.17, 7.0, 7.33, 7.63, 6.54, 8.54, 5.21, 4.46, 7.0, 6.5, 6.0, 7.25, 5.47, 7.63, 3.83, 4.92, 5.96, 6.33, 7.13, 4.61, 4.13, 7.0, 7.25, 7.88, 8.5, 5.21, 7.0]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The question to which we want to ask is: is there any significant difference between the scores obtained by the students that followed the classic or the alternative pedagogy? Does the alternative pedagogy have an impact on the obtained scores?\n",
"\n",
"Nice graphs can be obtained through the [seaborn](http://seaborn.pydata.org/) library. Data is therefore reorganized in a pandas DataFrame, in the format expected by seaborn (each line containing a single score and information about the section and group of the student)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Subject ID
\n",
"
Score
\n",
"
Group
\n",
"
Section
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
0
\n",
"
1.36
\n",
"
Classical
\n",
"
Petite
\n",
"
\n",
"
\n",
"
1
\n",
"
1
\n",
"
0.24
\n",
"
Classical
\n",
"
Petite
\n",
"
\n",
"
\n",
"
2
\n",
"
2
\n",
"
0.22
\n",
"
Classical
\n",
"
Petite
\n",
"
\n",
"
\n",
"
3
\n",
"
3
\n",
"
0.11
\n",
"
Classical
\n",
"
Petite
\n",
"
\n",
"
\n",
"
4
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4
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1.25
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Classical
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Petite
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"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Subject ID Score Group Section\n",
"0 0 1.36 Classical Petite\n",
"1 1 0.24 Classical Petite\n",
"2 2 0.22 Classical Petite\n",
"3 3 0.11 Classical Petite\n",
"4 4 1.25 Classical Petite"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tmp = []\n",
"for i, d in enumerate(data):\n",
" if i < 3:\n",
" group = \"Classical\"\n",
" else:\n",
" group = \"Alternative\"\n",
" \n",
" if i % 3 == 0:\n",
" section = \"Petite\"\n",
" elif i % 3 == 1:\n",
" section = \"Moyenne\"\n",
" else:\n",
" section = \"Grande\"\n",
" \n",
" for j, v in enumerate(d):\n",
" tmp.append([j, v, group, section])\n",
"df = pd.DataFrame(tmp, columns=[\"Subject ID\", \"Score\", \"Group\", \"Section\"])\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A first idea of the trend of the student scores can be obtained through a scatter plot, plotting the scores for each section. To analyze the difference between the two pedagogies, two scatter plots are produced."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"_, axs = plt.subplots(1, 2, figsize=(15, 6), sharey=True)\n",
"sns.scatterplot(data=df[df['Group'] == \"Classical\"], x=\"Section\", y=\"Score\", ax=axs[0])\n",
"axs[0].set_title(\"Scores for students following classical pedagogy\")\n",
"sns.scatterplot(data=df[df['Group'] == \"Alternative\"], x=\"Section\", y=\"Score\", ax=axs[1])\n",
"axs[1].set_title(\"Scores for students following alternative pedagogy\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The scores obtained by the two groups of students look quite similar for the same sections, maybe slightly lower for students in the *grande* section following alternative pedagogy. The variance in the groups is also remarkable, especially in the *moyenne* for classical pedagogy.\n",
"\n",
"The trend can be better visualized through lineplots, comparing the average evolution of students belonging to the same group. The standard deviation of the scores for the same group of students is represented by means of whiskers."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"_, ax = plt.subplots(figsize=(8, 6))\n",
"# ci='sd' plots standard deviation in seaborn (advanced Python data visualization library)\n",
"sns.lineplot(data=df, x=\"Section\", y=\"Score\", hue=\"Group\", err_style=\"bars\", ci='sd', ax=ax)\n",
"ax.set_title(\"Evolution of scores for different groups of students\")\n",
"plt.legend(loc='upper left', title=\"Group\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This representation gives an idea of the scores trend and confirms the previous impression (average score is similar in the two groups, slightly lower in *grande* section for the alternative pedagogy). However, the big variance does not allow to fully understand the students' evolution.\n",
"\n",
"A better representation, from the statistical point of view, can be obtained through box plots."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"_, ax = plt.subplots(figsize=(8, 6))\n",
"sns.boxplot(data=df, x=\"Section\", y=\"Score\", hue=\"Group\", ax=ax)\n",
"ax.set_title(\"Distribution of scores for different groups of students\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Thanks to this last visualization, one can better understand the distribution of the scores. We can easily conclude that there are no meaningful differences between the scores obtained by students following the two pedagogies."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Foot length/dictation mistakes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Procedure"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load data (merci guy on the Pad :) ):"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"data = {17.5: [15, 18, 19, 20], 18: [16, 17, 18, 19], 18.5: [14, 16, 17], 19: [15, 16], 20: [13, 14, 15], 20.5: [12, 13, 14], 21: [10, 11, 13, 15], 21.5: [10, 12, 13], 22: [8, 10, 11, 12], 23: [8, 9, 10], 23.5: [7, 8, 9, 11], 24: [6, 8, 9], 24.5: [6, 7, 8, 10], 25: [4, 6, 7, 8], 25.5: [5, 6], 26: [4, 5, 7], 26.5: [3, 4, 5], 27: [2, 3, 4, 7], 27.5: [2, 3], 28: [0, 1, 2, 4], 28.5: [0, 2], 29: [0, 1, 2]}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reformat data, in order to load it as Pandas dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Feet size
\n",
"
Number of mistakes
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
17.5
\n",
"
15
\n",
"
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"
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"
1
\n",
"
17.5
\n",
"
18
\n",
"
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"
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"
2
\n",
"
17.5
\n",
"
19
\n",
"
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"
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"
3
\n",
"
17.5
\n",
"
20
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"
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"
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"
4
\n",
"
18.0
\n",
"
16
\n",
"
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" \n",
"
\n",
"
"
],
"text/plain": [
" Feet size Number of mistakes\n",
"0 17.5 15\n",
"1 17.5 18\n",
"2 17.5 19\n",
"3 17.5 20\n",
"4 18.0 16"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tmp = []\n",
"for feet_size in data:\n",
" for mistakes_number in data[feet_size]:\n",
" tmp.append([feet_size, mistakes_number])\n",
"df = pd.DataFrame(tmp, columns=[\"Feet size\", \"Number of mistakes\"])\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The question to which we want to ask is: is there any important relation between the foot size of students and their number of dictation mistakes? To do so, we start by displaying the data with a scatter plot, considering the feet size as ordinal data:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"_, ax = plt.subplots(figsize=(15, 10))\n",
"sns.scatterplot(data=df, x=\"Feet size\", y=\"Number of mistakes\", ax=ax)\n",
"ax.set_title(\"Feet size vs. number of mistakes\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The scatter plot highlights a decreasing trend in the data. It can therefore be summarized through a line plot, displaying the feet size vs. the average number of mistakes. The variance information is also important, as it explains how the results vary for students with the same feet size, and is represented by means of whiskers."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
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"_, ax = plt.subplots(figsize=(15, 10))\n",
"# ci='sd' plots standard deviation in seaborn (advanced Python data visualization library)\n",
"sns.lineplot(data=df, x=\"Feet size\", y=\"Number of mistakes\", err_style=\"bars\", ci='sd', ax=ax)\n",
"ax.set_title(\"Feet size vs. average number of mistakes\")\n",
"plt.show()"
]
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"source": [
"### Comments"
]
},
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"source": [
"The above graph plots the foot size of the students, against the average number of mistakes made by students with that feet size. For each feet size, the standard deviation of the number of mistakes in the dataset is also represented by means of whiskers (this is done with the parameter `ci='sd'` in [seaborn](http://seaborn.pydata.org/)'s `lineplot`). This summarizes well the available data and gives an idea of the general trend.\n",
"\n",
"Intuitively, there should not be any correlation between the number of errors in dictation made by the students and their foot sizes. One can therefore expect not to see any clear trend. However, the graph shows the opposite: there is a negative correlation between the foot size and the number of mistakes. This suggests that students with larger foot are \"smartest\" than the ones with shortest foot.\n",
"\n",
"At a first impact, it looks like a case of \"spurious correlations\", like the ones that can be found on this [website](https://www.tylervigen.com/spurious-correlations), maybe amplified by the small size of the dataset. But, thinking about it a bit more, the trend is obvious and could be expected: students with biggest foot are very likely to be from the upper classes (foot of 28 centimeters corresponds to the European shoe size 45), while students with small foot come from primary education (18 centimeters is shoe size 29)!"
]
}
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