# 5 Statistical estimation

Far better an approximate answer to the right question … than an exact answer to the wrong question.22

Aside from hypothesis testing, one of the most common uses of statistical inference is the estimation of unknown parameters using sample data. Polling is one application where statistical estimation is used. For example, Gallup and the Pew Research Center are organizations that use statistical estimation to provide snapshots of public attitudes and opinions on topics from politics and the economy, to social awareness and health and well-being. The results of their polls are seen on a daily basis in almost every newspaper, news blog and website across the world.

Statistical estimation is used by more than pollsters. Biologists, social scientists, and medical researchers use statistical estimation to quantify population characteristics. For example, each year the Minnesota Department of Natural Resources estimates the populations of various species of animal, bird, and fish. These estimates are used to help set hunting and fishing regulations, as well as to allocate resources.23

### Outline and Goals of Unit 5

The following schematic outlines the course readings, in-class activities, and assignments for Unit 5.

Unit outline
5.1     🔨   Kissing the ‘right’ way
5.2     📖   Estimating, compatibility intervals, and margin of error
5.3     🔨   Cuddling with dogs
5.4     📖   Uncertainty and bias
5.5     🔨   CEO compensation
5.6     🔨   Comparing cuddling preferences
5.7     🔨   Statistical estimates in the news
5.8     🔨   Comparing estimates from random samples
5.9     🔨   Extension: Estimating effect size
5.10     🔨   Summative activity: Montana YRBS

In the readings, course activities, and assignments in Unit 5, you will explore the use of bootstrapping to make statistical estimates using TinkerPlots™. You will also learn about how sample size affects the precision/uncertainty of statistical estimates. Lastly, you will learn about the connections between hypothesis testing and estimation.

1. Tukey, J. (1962). The future of data analysis. Annals of Mathematical Statistics 33(1), 1–67.↩︎

2. Here is the Wolf Population report for 2016.↩︎