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central-limit-theorem.ipynb
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{ | |
"nbformat": 4, | |
"nbformat_minor": 0, | |
"metadata": { | |
"colab": { | |
"name": "central-limit-theorem.ipynb", | |
"provenance": [], | |
"authorship_tag": "ABX9TyMM/TTXyoa5WHWDuTys7I8D", | |
"include_colab_link": true | |
}, | |
"kernelspec": { | |
"name": "python3", | |
"display_name": "Python 3" | |
} | |
}, | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "view-in-github", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"<a href=\"https://colab.research.google.com/gist/jonkrohn/7ca39598a521f72a9be6fbd2f581fd66/central-limit-theorem.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "8NeC76SYxbw7", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"# The Central Limit Theorem" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "fQ0ummEfqwkj", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"## Load dependencies" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "aKFoeW-awwGZ", | |
"colab_type": "code", | |
"colab": {} | |
}, | |
"source": [ | |
"import numpy as np\n", | |
"import seaborn as sns\n", | |
"import statistics as stat" | |
], | |
"execution_count": 0, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "X4k8-W0QxkTH", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"## Simulating a normally-distributed population" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "DyWcncpqw_Vp", | |
"colab_type": "code", | |
"colab": {} | |
}, | |
"source": [ | |
"x = np.random.normal(size=10000) # at default parameters, will be \"standard\" normal distribution" | |
], | |
"execution_count": 0, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "yjl4rxygxHl8", | |
"colab_type": "code", | |
"outputId": "1df0f9ae-b945-4535-f29a-1b7022e6bf7c", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 282 | |
} | |
}, | |
"source": [ | |
"sns.distplot(x) " | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1b57ab1c50>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
}, | |
"execution_count": 3 | |
}, | |
{ | |
"output_type": "display_data", | |
"data": { | |
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GtkNN5KUmkB9lc9tMZvnsdACqbOFwc5Ws0JuI0NY3xO4z7WPztcfYzUM5qQkUZiRSZXPU\nm6tk4+hNWBp/kXX3mXZ8Gn1TEgdr+ex03jzWQkvPILPSE52OYyKMtehNRDjS0E1uagL56bHVbXPR\n8tkZKLCjyua+MVfOCr0Je31DXs60XWBFUXrMddtclJ+eSF5qgk1yZq6KFXoT9o419eDTsQU5Ytny\nonR2n+mgw0bfmCtkhd6EvSMN3WSnxFOYEdt90ytmZzDqU14/aq16c2Ws0Juw1j/s5VRrHytmx95o\nm/EKMxIpyU6y7htzxazQm7B2sdsmVkfbBBIRNi4v4N2aNnoGR5yOYyKIFXoT1o409JCV7GF2Zmx3\n21y0cUUhI6PKW8danI5iIkhQhV5ENopItYjUiMjjE+z/UxE5KiKHRORNEZkTsG9URA74v7aOP9eY\nSxkYHqWmxbptAq0tySQ/PYHtR5qcjmIiyKSFXkTcwFPAncAy4AERWTbusP1AuaquAl4Avh2wb0BV\n1/i/7saYIB0/38OoaszNbXM5LtdY980vT7TSP+x1Oo6JEMG06NcDNap6WlWHgS3ApsADVHWnqvb7\nn+4CikMb08Siww3dZCR5KM5KcjpKWNm4opDBER9vV7c6HcVEiGAKfRFQF/C83r/tUr4CbA94nigi\nlSKyS0TumegEEXnYf0xla6v94zUwODLKyZY+VsyO3ZukLmV9WTY5KfE2+sYELaRz3YjIF4Fy4JMB\nm+eoaoOIzAPeEpHDqnoq8DxVfRp4GqC8vFxDmclEpuPnexn1WbfNRNwu4fbl+Ww90MjgyCiJHrfT\nkUyYC6ZF3wCUBDwv9m/7CBG5Dfg6cLeqfrDumao2+P97GngbWDuFvCZGHGnoJj0xjpLsZKejhKWN\nKwq5MDzKOyfbnI5iIkAwhb4CWCgiZSISD9wPfGT0jIisBf6dsSLfErA9S0QS/I9zgRuBo6EKb6JT\n/7CXE829LJ+dgcu6bSZ0/bwc0hLjeNUmOTNBmLTrRlW9IvIYsANwA8+oapWIPAlUqupW4B+AVOB5\nf39qrX+EzVLg30XEx9gvlb9XVSv05rLeOdmG16cs8y+4YT4uPs7FZ5bm8/rRZkZGfXjcdkuMubSg\n+uhVdRuwbdy2JwIe33aJ894DVk4loIk9bx5rIdHjYm5OitNRwtrGFQW8uL+BXafbuWlhntNxTBiz\nhUdMWPH5lDePt7BwVhpul3XbXM7Ni/JIjnfzL2/WUNcx8LH9D24odSCVCUf2954JK4cbumnrG2JJ\nQZrTUcJeosfNLUtmUdXYzajPBquZS7NCb8LKm8eacQkszrdCH4y7Vs3mwvAop9v6nI5iwpgVehNW\n3jzewro5WSQnWK9iMD61OI+EOBeH6m3hcHNpVuhN2GjqHqCqsYdbl+Y7HSViJHrcLCtMp6qxG6/P\n53QcE6as2WTCxpv+qXdvXTKLirOdDqcJT5t3135s28riDPbXdVHT3MeSQhuSaj7OCr1x3MXi9eP3\nz5GdEs+eMx02v80VWDArlSSPm0MN3VbozYSs68aEhWGvj1OtfSwpSLMif4XiXC6Wz07naFMPI6PW\nfWM+zgq9CQunWvvw+pQlBdYivRorizIY9vo42Wyjb8zHWaE3YeH4+R4S4lzMzbVJzK7GvLxUEj0u\nqhpt9I35OCv0xnGqyvHzvSyclUqcy/5JXg23S1hakM6x8z1285T5GPupMo5r7Bqkd9BrFxKnaEVR\nBoMjPrt5ynyMFXrjuGPnexBgkd0NOyULZqUS73ZR1djjdBQTZqzQG8dVn++lJDuZVLsbdko8bheL\nCtI42tiDT637xnzICr1xVHPPIA1dAyy1ScxCYsXsdPqGvNS29zsdxYQRK/TGUW8dH7sbdrH1z4fE\n4vyx6Z2PNln3jflQUIVeRDaKSLWI1IjI4xPs/1MROSoih0TkTRGZE7DvIRE56f96KJThTeR781gz\nWcke8tMSnI4SFRI8bublplB9vtfpKCaMTFroRcQNPAXcCSwDHhCRZeMO2w+Uq+oq4AXg2/5zs4Fv\nABuA9cA3RCQrdPFNJBscGeWdmjYWF6Tb3bAhtLggjda+Ieu+MR8IpkW/HqhR1dOqOgxsATYFHqCq\nO1X14r+qXUCx//EdwOuq2qGqncDrwMbQRDeR7r1TbQyO+Kx/PsQuzuX/9okWh5OYcBFMoS8C6gKe\n1/u3XcpXgO1Xcq6IPCwilSJS2draGkQkEw3eONZCSrybslxbGzaUclITyEmJ/+D6hzEhvRgrIl8E\nyoF/uJLzVPVpVS1X1fK8PFvkOBaoKm8da+GmhXnEuW1MQKgtLkjj/VPtDAyPOh3FhIFgfsIagJKA\n58X+bR8hIrcBXwfuVtWhKznXxJ6qxh7O9wxyy9JZTkeJSovz0xjy+th1ut3pKCYMBFPoK4CFIlIm\nIvHA/cDWwANEZC3w74wV+cC/F3cAt4tIlv8i7O3+bSbGvVZ1HpeMLTJiQm9ubgpJHjc7q637xgSx\n8IiqekXkMcYKtBt4RlWrRORJoFJVtzLWVZMKPO8fPVGrqneraoeI/B1jvywAnlTVjmn5TkxE2VHV\nzLVzs8lJtWGV08HjdnHjghx2VregqjaqKcYFdc+5qm4Dto3b9kTA49suc+4zwDNXG9BEn7NtF6hu\n7uWJu8aP0jWhdPOiPN441kJtRz9zcuyCdyyzq2Bmxu2oOg/A7cttEfDpdMP8XADerbF++lhnhd7M\nuB1V51lRlE5xli0yMp3m56WQn57Au6fanI5iHGaF3syo5p5B9tV2cceyAqejRD0R4cb5ubx/qh2f\nLUYS06zQmxn12tFmADausEI/E25YkEvHhWGO29w3Mc0KvZlR2w83MS83hQWzUp2OEvU2766ltXfs\nlpbvvnWSzbtrHU5knGKF3syYlp5B3j/dzl2rZ9twvxmSkeQhNzWBmlZbXjCW2ZI+Zsa8fKgJVXAJ\n1rqcQfPzUthf24XX53M6inGItejNjNl6sJHZGYnMSkt0OkpMmZ+XyvCoj/qOAaejGIdYoTcz4lz7\nBQ7WdbGqONPpKDFnXl4KApyy7puYZYXezIitBxoBWFWc4XCS2JMcH8fszCQr9DHMCr2ZdqrK1oON\nXDs3i8zkeKfjxKT5eSnUdQzQP+x1OopxgBV6M+0ON3RzsqWPTWsut16NmU7z81IZVWXPGZtTMBZZ\noTfT7icVdSTEufjN1bOdjhKz5uSk4HYJ79bYdAixyAq9mVYDw6NsPdDIb6wsJCPJ43ScmBUf56I0\nO9kmOItRVujNtHq1qoneIS9fKC+Z/GAzrebnpXK0qYeOC8NORzEzzAq9mVY/qaijNDuZDWXZTkeJ\neQvyxuakf/+UtepjTVCFXkQ2iki1iNSIyOMT7L9ZRPaJiFdEPjdu36iIHPB/bR1/role59ovsOt0\nB18oL8blsikPnFaUlUxqQpxNWxyDJp0CQUTcwFPAZ4B6oEJEtqrq0YDDaoEvAX8+wUsMqOqaEGQ1\nEea5yjpcAp9bZ9024cDtEjaUZfOeXZCNOcG06NcDNap6WlWHgS3ApsADVPWsqh4CbDINA8CoT3lh\nbz2fXJRHQYZNeRAubliQy9n2fuo7+52OYmZQMJOaFQF1Ac/rgQ1X8B6JIlIJeIG/V9WfjT9ARB4G\nHgYoLS29gpc24erJl6to7hni1iVJNoFZGPnEgrHlBd+raecL19oKX7FiJi7GzlHVcuBB4J9FZP74\nA1T1aVUtV9XyvLy8GYhkplvluU5S4t0sKUxzOooJsCg/ldxUW14w1gRT6BuAwE7WYv+2oKhqg/+/\np4G3gbVXkM9EoLa+IY419bC2NIs4lw3sCiciwg3zc3jvVDuqtrxgrAjmp7ACWCgiZSISD9wPBDV6\nRkSyRCTB/zgXuBE4evmzTKR7aV8DPoXyOVlORzETuHFBDq29Q5xssUnOYsWkhV5VvcBjwA7gGPCc\nqlaJyJMicjeAiFwrIvXA54F/F5Eq/+lLgUoROQjsZKyP3gp9FFNVnt9bR0lWErPS7SJsOLph/lg/\nvU2HEDuCWmFKVbcB28ZteyLgcQVjXTrjz3sPWDnFjCbMBV5sbega4ERzH5vW2Lw24aokO/mD6RC+\nfGOZ03HMDLAOVBNS+2s7cbuEVUW2wEg4u3FBDrtPt+MdtRHRscAKvQmZUZ9ysL6bpQVpJMW7nY5j\nJrB5d63/LzChd8jLt16tdjqSmQFW6E3InGzp5cKQl7WldhE23C2clYpbhOPne5yOYmaAFXoTMvtr\nu0iOd7MwP9XpKGYSiR43ZbkpHG/qdTqKmQFW6E1IDAyPcqyph1XFmTZ2PkIsKUyjtW+IM20XnI5i\nppn9RJqQONLYjdenXFNqF2EjxdKCdADePNbscBIz3azQm5DYX9tJXmoCRZlJTkcxQcpKiacgPZE3\nrNBHPSv0Zso6Lgxztr2ftaWZiNi885FkSUEaFWc76e4fcTqKmUZW6M2UHajrBGBNiXXbRJqlhemM\n+pSd1S1ORzHTyAq9mRJVZX9tF2W5KWQmxzsdx1yhoqwk8tMTeOVQo9NRzDSyQm+mZH9dF+0Xhu0i\nbIRyiXDPmiLerm6lrW/I6ThmmlihN1Py4r56PG5h+ewMp6OYq3TvumK8PuXnB6xVH62s0JurNuQd\n5ZVDTSwtTCfRY1MeRKpF+WmsLMrgxX31Tkcx08QKvblqO4+30tU/wjU25UHEu/eaIqoae2xKhChl\nhd5ctRf31ZObmsD8PJvyINLdvaYIj1v46V5r1UcjK/TmqnReGGZndQv3rJmN22Vj5yNddko8n148\ni5f2NzA4Mup0HBNiQRV6EdkoItUiUiMij0+w/2YR2SciXhH53Lh9D4nISf/XQ6EKbpz1yqFGRkaV\n37rmY+vNmAj15RvLaOsb5vnKOqejmBCbtNCLiBt4CrgTWAY8ICLLxh1WC3wJ2Dzu3GzgG8AGYD3w\nDRGxDt0o8OL+BpYUpLFsdrrTUUyIXDcvm3VzsvjeL08zYguSRJVgWvTrgRpVPa2qw8AWYFPgAap6\nVlUPAeP/ddwBvK6qHaraCbwObAxBbuOg06197K/t4reuKXI6igkhEeHRT8+noWuAn+1vcDqOCaFg\nCn0REPi3XL1/WzCCOldEHhaRShGpbG1tDfKljVNe2t+AS2DTGiv00ebTi2exrDCdf3v7FKM+dTqO\nCZGwuBirqk+rarmqlufl5Tkdx1yGz6e8tL+BGxfkkp+e6HQcE2JjrfoFnG67wEvWqo8awRT6BqAk\n4Hmxf1swpnKuCUP/e/tx6jsHKMxIDFh/1ESTO1cUsKYkk7/ffsxmtYwSwRT6CmChiJSJSDxwP7A1\nyNffAdwuIln+i7C3+7eZCLW/tpN4t4tlhTblQbRyuYT/dc8KOi4M8w+vHXc6jgmBuMkOUFWviDzG\nWIF2A8+oapWIPAlUqupWEbkWeAnIAn5TRP5WVZeraoeI/B1jvywAnlTVjmn6Xsw0GxwZ5XBDNyuK\n0omPC4tePzNNVhRlsGFeDs/uqiU90UNxVjIAD24odTiZuRqTFnoAVd0GbBu37YmAxxWMdctMdO4z\nwDNTyGjCxGtHmxny+lhTYiNkY8FnluZzpKGbl/Y38IefnE+c2365R6qgCr0xAP+9u5bMZA/z8lKc\njmJC6FLXWRI9bu5ZU8SPd53jzeMt3LG8YIaTmVCxX9EmKKdb+3j/dDvXzs3GZcsFxoylhemUz8ni\nVydaOdt2wek45ipZoTdB+e89tcS5hHVzrNsm1nx2ZSGZyR6e31tH35DX6TjmKlihN5MaHBnlhb31\n3LY0n/REj9NxzAxL8Lj5QnkJXf0j/N3LR52OY66CFXozqR1V5+nsH7ERFzFsTk4KNy/K4yeVdbx+\ntNnpOOYK2cVYM6n/2nWO0uxkPrEgly0VNrNhrLp16SxONPfyJ1v289XbFpGa8GH5sEZAeLMWvbms\nirMdVJzt5KEb5uKyeedjWpzLxefLSxjy+nhpfwOqNhdOpLBCby7rX3fWkJ0SzwPrSyY/2ES9gvRE\nbl+Wz7GmHvbVdjodxwTJCr25pKrGbnZWt/J7N84lOd56+cyYGxbkMi83hZcPNdFxYdjpOCYIVujN\nJf3rzlOkJcTxO9fPdTqKCSMuET63rhgBXthbh8+6cMKeFXozoRPNvWw70sTvXD+HjCQbUmk+KjM5\nnrtWzeZsez97ztj0VeHOCr35GFXliZ8fIT3Rw+/fNM/pOCZMXVOaycJZqbxadZ6GrgGn45jLsEJv\nPublQ03sOt3BX9yxmOyUeBqFGE4AAAyhSURBVKfjmDAlItyzpggUvv7SYRuFE8bsCpv5iL4hL9/8\nxVFmZ46tHmULi5jLyUqJ5/bl+bxyqImfHWjgf6ydcBJb4zBr0ZuP+Mcd1TT3DHH3qtk2eZkJynXz\nclg3J4u/ffkobX1DTscxE7BCbz7wWtV5fvTeWb50w1xKc2wqYhMclwjfuncl/UOj/K3NhROWgir0\nIrJRRKpFpEZEHp9gf4KI/MS/f7eIzPVvnysiAyJywP/1vdDGN6FS19HPnz9/kJVFGXztN5Y4HcdE\nmAWz0vijWxbw8sFGmwsnDE1a6EXEDTwF3AksAx4QkWXjDvsK0KmqC4B/Ar4VsO+Uqq7xfz0Sotwm\nhAaGR3ls8z5U4akHryEhzu10JBOBHvnUfJYUpPFXLx22G6nCTDAt+vVAjaqeVtVhYAuwadwxm4D/\n43/8AnCriHXwRoJhr48/fHYvhxu6+X++sJrSnGSnI5kI5XG7+Kf71tDdP8JfvWijcMJJMKNuioDA\nKQvrgQ2XOsa/mHg3kOPfVyYi+4Ee4K9V9ddTi2xCZdSn/NnzB3m7upX//Vsrud2WijNXKXB01i1L\nZvFq1Xle3NfAvetsFE44mO7hlU1Aqaq2i8g64GcislxVewIPEpGHgYcBSkttutOZoKp8Y+sRXj7Y\nyMblBajaUEoTGp9YmMvx8z18Y2sV6+ZkMTfXLuw7LZiumwYgcOrCYv+2CY8RkTggA2hX1SFVbQdQ\n1b3AKWDR+DdQ1adVtVxVy/Py8q78uzBB2by79oOvL/+ogv/aVcvNC/O4eZF95iZ0XCJ8vrwEt0v4\nv5/dx+DIqNORYl4whb4CWCgiZSISD9wPbB13zFbgIf/jzwFvqaqKSJ7/Yi4iMg9YCJwOTXRztd45\n2crb1a1cOzeLO5bnOx3HRKGs5Hj++b41HG3q4W+2VjkdJ+ZN2nXj73N/DNgBuIFnVLVKRJ4EKlV1\nK/BD4MciUgN0MPbLAOBm4EkRGQF8wCOqajMgOWjvuQ62HTnPiqIMNq0pwq6Zm+nS1D3IpxbnsaWi\njmGvj/K52bYSlUOC6qNX1W3AtnHbngh4PAh8foLzfgr8dIoZTYhUNXbz4r4GFs5K5Qvriu3OVzPt\nbluaT0PnAD870ECWzZvkGLszNkb86kQrWyrqKM5K4rc3zCHObf/rzfRzifDA+lJyUxN4dvc5TrX2\nOR0pJtlPewx471Qbf/CflcxKS+ChG+YSH2f/283MSfS4eej6ubhdLr70H3to7hl0OlLMsZ/4KFdx\ntoOv/KiSOTnJfPnGMlsS0DgiKyWeh66fQ0ffMF/8wW67c3aGWaGPYvtrO/nyf1RQmJnIs79/HakJ\nVuSNc4qzkvnhl66ltqOf331mN90DI05HihlW6KPU4fpufveZPeSkxrP5968jLy3B6UjGcN28HL73\nO+uoPt/Lb/9gl7XsZ4gV+ii050wHX/zhbjKSPGz+g+soyEh0OpIxH/j04ll8/3fLOdncx/1Pv09L\nr/XZTzcJt4mHysvLtbKy0ukYEevlg4382XMHSU/y8OUb5tqQNhO2TrX28eP3z5GaGMfzj1zP/LxU\npyNFNBHZq6rlE+2zFn2U8I76+M5r1fzRf+9ndUkGj3xynhV5E9bm56XylU+UMTQyyr3/9h4VZ+1e\nyulihT4KNHYN8MD3d/H/vVXD59YV8+OvbLDRNSYilGQn88gn55OVHM9vf383P6mwifWmgxX6COYd\n9fHDd85w+z/9iqONPfzzfWv4x8+vJtFjC4eYyJGTmsCLf3gD68uy+cufHuavXjrMkNcmQgsl66OP\nQKrK2yda+db24xw/38ui/FTuXl1EtnXVmAjmU+W1qmZ+dbKVwoxEfvTl9SwuSHM6VsS4XB+9/X0f\nQZ7ddY6alj7eOt7CuY5+spI9PLi+lOWz021yMhPxXCJsXFHAnJxkXtxXz29+9x3+/PZFfPnGMjw2\nZceUWIs+AvQPe3nlUBPfee0E53sGSU+M49NLZrFuThZxLvsBMNGnb8jLnjMdvHGsmUX5qfzN3cu5\nYX6u07HCmrXoI5B31Meesx384lATWw800jvkZVZaAvdeU8zq4gyblMxEtdSEOL7/u+t4/WgzT75y\nlAe/v5ubFubyR7csZH1ZttPxIo616MNI35CXX1a38vrR8+ysbqV7YISEOBefXVnIfdeWUNPSZ100\nJuaMjPp4/1Q7v65p48KQl9UlmdxXXsJdqwtJT/Q4HS9sXK5Fb4XeQReGvOw918nuM+3sOt3Bofou\nRkaVrGQPtyzJ5zPL8rlpYS4p/jlqbE1XE8uGvT5A2bynlhPNfcTHudhQls0nF+Wxbk4WSwvTY3rE\nmRX6MNHdP8L+uk52ne5g95l2Dtd34/UpLoGizCTKclNZXJBGaXYybpe13I2ZiKrS0DXAofpuTjT3\n0tI7BIDbJczJSaYoM4nZGUm0XRgiMymejCQPmUkeMpI9PHTDXGfDT6MpF3oR2Qj8v4wtJfgDVf37\ncfsTgP8E1gHtwH2qeta/72vAV4BR4I9Vdcfl3isaCr2q0n5hmLNtFzjc0M3Bui4O1ndzpu0CAG4R\nirKSmJebQlluCqU5ySTExW5LxJip6B4YoSw3hSMN3Zxu66Oha5DGrgFa/b8AAmUleyjMSGJ2ZiKF\nGUkUZiaO/WLwf+WnJUTs9a8pXYz1L+79FPAZoB6oEJGtqno04LCvAJ2qukBE7ge+BdwnIssYWz92\nOTAbeENEFqnqjN4Noar4dGycrk8V9T8e9Skjo8qw18eQd9T/Xx/Do74PH1/8Gh39yLaP/3eUlt4h\n6jr6qe3op3/4w28xPz2B1cWZLJyVSnFWMqXZybb4hzEhkpHkYeOKAjauKPjI9v987yzdAyN0DYzQ\nPTBCz8XH/SMcaejh3Zp2BkY+WorcLiErOZ7M5LG/AjL9j5Pj3SR63CTGuUjw+B97XCTGBTz2/zch\nzk18nAuP20WcSz587Bbi/dvcLpnR623BjLpZD9So6mkAEdkCbAICC/0m4G/8j18Avitj38UmYIuq\nDgFn/IuHrwfeD038D7X3DXHTt3f6i/lHi/t09k65RUj0uIiPczErLZGS7GRumJ9LS+8g2cnxFGYm\nkZFkF4yMmU4TXb+Kc7vISU0gJ/XSU3QPeUfp7v/wF0DnwDAXhrz0D4/ScWGY+s4BBkbGGnlen4+R\n0dAUExGIcwmCgIBLQBBWl2Sw5eHrQ/IegYIp9EVAXcDzemDDpY5RVa+IdAM5/u27xp1bNP4NRORh\n4GH/0z4RqQ4q/ZXJBdqm4XWjiX1Gl2efz+TsM5rcJT+j48BP/q+rft05l9oRFuPoVfVp4OnpfA8R\nqbxU/5UZY5/R5dnnMzn7jCbnxGcUTEdxA1AS8LzYv23CY0QkDshg7KJsMOcaY4yZRsEU+gpgoYiU\niUg8YxdXt447ZivwkP/x54C3dGw4z1bgfhFJEJEyYCGwJzTRjTHGBGPSrht/n/tjwA7Ghlc+o6pV\nIvIkUKmqW4EfAj/2X2ztYOy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"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "itL991euxJh3", | |
"colab_type": "code", | |
"outputId": "741dd793-9fb2-4500-91d8-2790d224c3c3", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 282 | |
} | |
}, | |
"source": [ | |
"sns.distplot(x, kde=False)" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1b57a30080>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
}, | |
"execution_count": 4 | |
}, | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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kU3UKWpKzkzyYZHeSSyddz6GSfDrJgST3TLqWuSRZl+S2JPe1v+dLJl3ToZI8L8k3knyn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\n1CHDXZI69P/2TbLS8N+2UwAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "CBtcBnrczDIn", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"## Sampling from the normally-distributed population" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "VZ5xt8-_xNK9", | |
"colab_type": "code", | |
"outputId": "325b625a-b526-41ad-d024-6335a39cd65b", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 52 | |
} | |
}, | |
"source": [ | |
"x_sample = np.random.choice(x, size=10, replace=False)\n", | |
"x_sample" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"array([-0.98305864, -1.30834182, 0.27523993, 0.83480235, -0.60122655,\n", | |
" 1.1563367 , -0.10724061, -0.07554101, 0.32004786, -1.37220148])" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
}, | |
"execution_count": 5 | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "CJFXZCeozJeT", | |
"colab_type": "code", | |
"outputId": "da4fc115-d9a7-4dca-9b6c-3c211cf9dc66", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 34 | |
} | |
}, | |
"source": [ | |
"stat.mean(x_sample)" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"-0.18611832729025957" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
}, | |
"execution_count": 6 | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "x1AUauRd0Nrz", | |
"colab_type": "code", | |
"colab": {} | |
}, | |
"source": [ | |
"def sample_mean_calculator(population_array, sample_size, n_samples):\n", | |
" sample_means = []\n", | |
" for i in range(n_samples):\n", | |
" sample = np.random.choice(population_array, size=sample_size, replace=False)\n", | |
" sample_mean = stat.mean(sample)\n", | |
" sample_means.append(sample_mean)\n", | |
" return sample_means" | |
], | |
"execution_count": 0, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "KlxZoBfJpo5t", | |
"colab_type": "code", | |
"outputId": "f6da0aeb-bd87-417e-89ec-dbf8c817ae5b", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 265 | |
} | |
}, | |
"source": [ | |
"_ = sns.distplot(sample_mean_calculator(x, 10, 10))" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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pdSSlfN6dl2QzKSueR1fupby20eo4PsGjB0VF5A4gF3iis/XGmMXGmFxjTG5y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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "-zCr0WLpqZAG", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"###### The more samples we take, the more likely that the sampling distribution of the means will be normally-distributed: " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "k1xLRkhO2IsJ", | |
"colab_type": "code", | |
"outputId": "3222f31a-bf77-4e5d-a336-28db6f1c769f", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 265 | |
} | |
}, | |
"source": [ | |
"_ = sns.distplot(sample_mean_calculator(x, 10, 1000))" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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wLy+FysYu+p0uq8tRQcqrJpIxZj2wfsRj3xt2+xCwwrelqXD0TsVQL9zsHO0/H695+cm8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\nKaXChAa6UkqFif8PqTceoqFZcvgAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "mY2FO1oM1WDt", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"###### The larger the sample, the tighter the sample means will tend to be around the population mean:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "sQuio0HW2MYX", | |
"colab_type": "code", | |
"outputId": "05e66cb2-be6a-423c-d561-dc1d35c722df", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 265 | |
} | |
}, | |
"source": [ | |
"_ = sns.distplot(sample_mean_calculator(x, 1000, 1000))" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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U6jjqAkG3wI0xbwB6VyxKnGjt5/HddaS7Etjy4BUsKdSWtwqP1WVZ/Pk18/nh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\n5w7/VZ6hAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "3zEvgjiK3Uxp", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"## Sampling from a skewed population" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "XG_UK4c83tYS", | |
"colab_type": "code", | |
"colab": {} | |
}, | |
"source": [ | |
"from scipy.stats import skewnorm" | |
], | |
"execution_count": 0, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "7KVlRdJZ2Z1i", | |
"colab_type": "code", | |
"colab": {} | |
}, | |
"source": [ | |
"s = skewnorm.rvs(12, size=10000)" | |
], | |
"execution_count": 0, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "UA31KMPj39Ro", | |
"colab_type": "code", | |
"outputId": "6a8180f9-10e5-4bb3-8848-4d4efffcbb00", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 282 | |
} | |
}, | |
"source": [ | |
"sns.distplot(s)" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1b57210e48>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
}, | |
"execution_count": 13 | |
}, | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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ihR4h/mCIbv9AQl4lOpo5BRlccVYBP3v5LbvQyJgpYoUeIR22wuUkH71kFi1d/Ty9s8np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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "ZbPOIfhB4Rbb", | |
"colab_type": "code", | |
"outputId": "ccd028aa-5d2f-425f-d244-3bac0dd4fe44", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 265 | |
} | |
}, | |
"source": [ | |
"_ = sns.distplot(sample_mean_calculator(s, 10, 1000))" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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Gx+hNL1RQaKErWnqGGHF79YKiABARKvOSeXN/G0OjHqvjqAinha44pBcUBdRZ+SkMjHiO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\nRK5CYII=\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "pv9QwwwH3_ye", | |
"colab_type": "code", | |
"outputId": "595420cf-2152-4e8f-c6b1-0b7a1d8408d3", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 265 | |
} | |
}, | |
"source": [ | |
"_ = sns.distplot(sample_mean_calculator(s, 1000, 1000))" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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DbtTFrbyMFG6vKubpfRcZ99k0g7Em3BJ9KbBbRA4D7wDPq+ovReQhEXnI2ecF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\nDdd7F/CEqi4G7ibQ/yMWvh9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| |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "lV4IdTBY4gOl", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"## Sampling from a multimodal distribution" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "mWDy1Xm74i6p", | |
"colab_type": "code", | |
"colab": {} | |
}, | |
"source": [ | |
"m = np.concatenate((np.random.normal(size=10000), np.random.normal(loc = 4.0, size=10000)))" | |
], | |
"execution_count": 0, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "x3giYmlL4XLg", | |
"colab_type": "code", | |
"outputId": "1a721f96-08a1-4541-89cf-0ab31c23c492", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 265 | |
} | |
}, | |
"source": [ | |
"_ = sns.distplot(m)" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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mZ4BvqeppEXlURO4BEJFbRMQPvA/4ooicds7tA/6EcBI5BDzqbAP4TeCfgCbg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\nJQBjjElR/z/fZuO8r7Rp0AAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "RBOvq4cX5Rge", | |
"colab_type": "code", | |
"outputId": "9685afd0-d7d1-4c37-c91e-dcbeb5af18d5", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 265 | |
} | |
}, | |
"source": [ | |
"_ = sns.distplot(sample_mean_calculator(m, 1000, 1000))" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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C5vWmPlRkJSEzyap1lIh096YCxJuN+Nm+M1pHIY3NWdQiYhWRgyJSIyK1IvKv4QhGkc3t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\nqQAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "Hy0cr3NC6m86", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"## Sampling means from *any* distribution produces a normal sampling distribution" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "hX9q4P9363wU", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"###### Even when sampling from a uniform population:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "RLUCRb8F6Vx0", | |
"colab_type": "code", | |
"colab": {} | |
}, | |
"source": [ | |
"u = np.random.uniform(size=10000)" | |
], | |
"execution_count": 0, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "qc9M4F88nT1P", | |
"colab_type": "code", | |
"outputId": "ada2632c-ceb2-47e4-a50e-584560b64227", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 265 | |
} | |
}, | |
"source": [ | |
"_ = sns.distplot(u, kde=False)" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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WtRLuw1zS4DCwr7XfAtxU7Z2KNWrROSd5NfDnDIJ9ra/DwiJzrqqTVbWpqrZX1XYG7zO8\nuapmVqfckRjme/tvGRy1k2QTg2WaByZZ5IgNM+evArsBkvwYg3Cfm2iVk3UYuKydNbMLOFlVD6/o\nEVf7XeQlvNu8h8ERy5eB97W+P2Dwww2DF/+vgFngc8BLV7vmCcz5n4FHgDvb1+HVrnnccz5l7C2s\n8bNlhnydw2A56j7gbuDS1a55AnPeCXyWwZk0dwJvWu2aVzjfTwAPA99h8JfYfuBdwLvmvcYfav8e\nd4/i+9rLD0hSh9bKsowkaQkMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktSh/wcJsTi+Ry3MqAAA\nAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "DGvvpf_v7CVu", | |
"colab_type": "code", | |
"outputId": "8a84627c-3357-4e8d-e775-b6e6afc52548", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 265 | |
} | |
}, | |
"source": [ | |
"_ = sns.distplot(sample_mean_calculator(u, 1000, 1000))" | |
], | |
"execution_count": 0, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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rtjqKstisjkIxxniAN4FtQIqInF8MqwBoXOBsSr1PbccA7X0jbInQ+e/zRIQNxak0dA/R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\nU1rgSillU1rgSillU/8fptOMacHdvG0AAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "jSZBVTmv7Rzf", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"Therefore, with large enough sample sizes, we can assume the sampling distribution of the means will be normally distributed, allowing us to run statistical tests that are configured for normal distributions. All of the most popular statistical tests are configured this way.\n", | |
"\n", | |
"As an example of such a statistical test, the \"*t*-test\" allows us to infer whether two samples come from different populations (say, an experimental group and a control group). Thanks to the central limit theorem, we can use this test *even if we have no idea what the underlying distribution of the population is*, which is most of the time." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"id": "EWnrd1bd8JiR", | |
"colab_type": "code", | |
"colab": {} | |
}, | |
"source": [ | |
"" | |
], | |
"execution_count": 0, | |
"outputs": [] | |
} | |
] | |
} |
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