# A Tutorial on Fisher Information

## TL;DR

This tutorial explains the concept of Fisher information across frequentist, Bayesian, and minimum description length paradigms, highlighting its roles in hypothesis testing, prior definition, and model complexity measurement.

## Contribution

It provides a clear, unified explanation of Fisher information's applications in three different statistical paradigms, enhancing understanding for researchers.

## Key findings

- Clarifies Fisher information's role in hypothesis testing and confidence intervals
- Shows how Fisher information defines default priors in Bayesian analysis
- Demonstrates Fisher information as a measure of model complexity in MDL

## Abstract

In many statistical applications that concern mathematical psychologists, the concept of Fisher information plays an important role. In this tutorial we clarify the concept of Fisher information as it manifests itself across three different statistical paradigms. First, in the frequentist paradigm, Fisher information is used to construct hypothesis tests and confidence intervals using maximum likelihood estimators; second, in the Bayesian paradigm, Fisher information is used to define a default prior; lastly, in the minimum description length paradigm, Fisher information is used to measure model complexity.