{
 "metadata": {
  "name": "",
  "signature": "sha256:448d3166c863013e4d0f1da36e5e9d40fa46db19b24a5dd859045198cf29a708"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#All models are wrong, some models are useful"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Different forms of bias\n",
      "- selection bias - picking and choosing certain parts\n",
      "- publication bias (file drawer problem)\n",
      "- censoring bias\n",
      "- length bias\n",
      "- sampling bias - not feasible to sample every one, pay a lot attention to how samples are obtained\n",
      "\n",
      "###Why sample from a population?\n",
      "- often the only feasible way\n",
      "- general meta-question: what would you do if you had all the data?\n",
      "- important for computational reasons\n",
      "\n",
      "###Many sampling schemes\n",
      "- simple random sampling - complete random sample\n",
      "- stratified sampling - population divided into different groups \n",
      "- cluster sampling - pick a random cluster and sample everyone in that cluster\n",
      "- snowball sampling - relevant for network, to reach hard to reach population\n",
      "\n",
      "###Absolute vs Relative\n",
      "- in simple random sampling, which matters more, relative or absolute sample size?\n",
      "- absolute matters much more than relative (example sampling for a bowl of soup vs pot)\n",
      "\n",
      "###Bias of an Estimator\n",
      "- bias of an estimator is how far off it is on average\n",
      "- why not substract off the bias?\n",
      "- Consider bias-variance trade-off\n",
      "- When model gets more complicated, you overfit, lower bias but variance increase\n",
      "\n",
      "###How to combine independent estimators for a parameter into 1 estimator?\n",
      "- Average those numbers (simplistic)\n",
      "- Giving them weights, all weights should sum to 1\n",
      "- How to choose weights? The higher standard error, the less weight\n",
      "- Weights should be inversely proportional to variance\n",
      "- Important to consider what weights to use"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}