ch1_the-golem-of-prague.Rmd

---
title: "Chapter 1. The Golem of Prague "
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

The first two sections are spent describing some of the general problems that statisticians and researchers face is designing statistical tests and models.

## 1.3 Tools for golem engineering

* use models for several distinct purpose:
    - designing inquiry
    - extracting information from data
    - making predictions
* this book focuses on the following tools towards these purposes:
    - Bayesian data analysis
    - model comparison
    - multilevel models
    - graphical causal models
* this book focuses mostly on code - how to do things ("golem engineering")

### 1.3.1 Bayesian data analysis

* Bayesian data analysis takes a question in the form of a model and uses logic to produce an answer int he form of probability distributions.
* it is like counting the number of ways the data could happen according to some assumptions
    - things that can happen more ways are more plausible

### 1.3.2 Model comparison and prediction

* there are many ways to compare models
* we will learn about "cross-validation" and "information criteria" as metrics of predictive power of a model
* this will introduce the phenomenon of more complex models making worse predictions: "over-fitting"

### 1.3.3. Multilevel models

* models contain parameters which can sometimes stand-in for other missing models
    - given some model of how the parameter gets its value, the new model can be inserted in place of the parameter
    - this creates a final model with multiple levels of uncertainty
* these models are also called "hierarchical," "random effects," "varying effects," or "mixed effects" models
* multilevel models can help fight over fitting using "partial pooling" (covered in Chapter 13)
* they generally apply when there are clusters or groups of measurements that may differ from one another

### Graphical causal models

* one form of prediction, mentioned above, is what will the outcome be in the future
* another type is causal prediction: what process causes the other
    - this is essential knowledge for using a model to intervene in the world