# 2 Modeling Sampling Variation

In the course activities and homework assignments, you have been using probability models to generate random outcomes. You have also learned how to use Monte Carlo simulation to generate many data sets from a given model. This is the same kind of process that researchers, scientists, and statisticians engage in when they evaluate (or test) hypotheses about the world.

### Outline and Goals of Unit 2

The following schematic outlines the course readings and in-class activities for Unit 2.

Unit outline |
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2.1 📖 Introduction to statistical hypothesis testing |

2.2 🔨 Monday breakups |

2.3 📖 Null hypotheses |

2.4 📖 A closer look at statistical hypothesis testing |

2.5 🔨 Facial prototyping |

2.6 🔨 A preference for 7? |

2.7 🔨 Gender and congress |

2.8 🔨 Review activity: Racial disparities in police stops |

2.9 📖 Unit 2 summary |

In the readings, course activities, and assignments in Unit 2, you will explore the process of evaluating statistical hypotheses. You will be introduced to several common models that used by researchers and statisticians. You will also use TinkerPlots™ to generate simulated data to study the variation in results that would be expected under these models. Many of these models are directly related to the chance models that you have explored in the course to this point. For example, you should already be able to use TinkerPlots™ to produce results that would be expected from 100 flips of a “fair” coin.

In addition to evaluating hypotheses, you will learn about common misconceptions regarding model evaluation (e.g., we can never say a model produced the data, only that it produces results compatible with the data), and how to use probabilistic language when providing an “answer” to a research question.