Created
January 24, 2022 16:39
-
-
Save drbenvincent/fdc92b47dbdc05248e9ecc299afeac0b to your computer and use it in GitHub Desktop.
code for blog post https://drbenvincent.medium.com/the-dunning-kruger-effect-probably-is-real-9c778ffd9d1b
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import numpy as np\n", | |
"from scipy.stats import norm\n", | |
"import matplotlib.pyplot as plt" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def generate_data(N=1_000_000, bias=0, σo=1, σs=1):\n", | |
" # true ability\n", | |
" x = norm.rvs(size=N)\n", | |
" # objective measure of ability\n", | |
" o = x + norm.rvs(loc=0, scale=σo, size=N)\n", | |
" # subjective measure of ability\n", | |
" s = x + bias + norm.rvs(loc=0, scale=σs, size=N)\n", | |
" # group participants into quartiles based on objective measure\n", | |
" q = np.digitize(o, np.percentile(o, [25, 50, 75])) + 1\n", | |
" return (x, o, s, q)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def plot_subjective_utility(o, s, q, ax):\n", | |
" # Calculate means for each quartile\n", | |
" s_mean = [np.mean(s[q == group]) for group in [1, 2, 3, 4]]\n", | |
" o_mean = [np.mean(o[q == group]) for group in [1, 2, 3, 4]]\n", | |
" # Convert to percentiles, based on the observed score\n", | |
" s_mean = norm.cdf(s_mean, loc=0, scale=np.std(o_mean)) * 100\n", | |
" ax.plot([1, 2, 3, 4], s_mean, \"o-\", lw=6, ms=12,\n", | |
" label=\"subjective ability\")\n", | |
"def format_quartile_plot(ax=None):\n", | |
" ax.plot([1, 4], [12.5, 87.5], \"k-\", label=\"identity line\")\n", | |
" ax.set(\n", | |
" xlabel=\"Quartile of observed performance\",\n", | |
" ylabel=\"Percentile estimate\",\n", | |
" xticks=[1, 2, 3, 4],\n", | |
" yticks=np.linspace(0, 100, 11),\n", | |
" ylim=[0, 100],\n", | |
" )" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": "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\n", | |
"text/plain": [ | |
"<Figure size 432x432 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"fig, ax = plt.subplots(figsize=(6, 6))\n", | |
"x, o, s, q = generate_data(bias=0, σo=2, σs=2)\n", | |
"plot_subjective_utility(o, s, q, ax)\n", | |
"format_quartile_plot(ax)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": "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\n", | |
"text/plain": [ | |
"<Figure size 432x432 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"fig, ax = plt.subplots(figsize=(6, 6))\n", | |
"# Define parametes, each tuple is (m, c, σo, σs)\n", | |
"parameter_set = [(0, 2, 2), (+1, 2, 2), (-1, 2, 2)]\n", | |
"for θ in parameter_set:\n", | |
" bias, σo, σs = θ\n", | |
" x, o, s, q = generate_data(bias=bias, σo=σo, σs=σs)\n", | |
" plot_subjective_utility(o, s, q, ax)\n", | |
"format_quartile_plot(ax)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"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": "3.7.4" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 4 | |
} |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment