1 Introduction

1.1 Intro

The intended use of this text is to introduce the most useful types of statistical analysis including linear models and their generalized linear model (GLM) extensions
The approach is to learn by doing using real datasets relating to biological and environmental sciences

The focus is on linear model framework since this applies to biological sciences well
GLMs are introduced for non-normal distributions

Non-linear regression approaches, generalized additive models, non-parametric approaches

Most of the methods in this text belong to the ‘classist frequentist statistics’
This approach has come under scrutiny due to its reliance on probability (p) values and lowered emphasis on effect sizes (estimates and intervals)
The author is also pretty vocal about this criticism so he uses estimation-based approaches that focuses on estimates and confidence intervals where possible
He also uses a priori contrasts (comparisons that were planned in advance) and encourages avoidiing the inappropriate overuse of multiple testing and instead to implement a more thoughtful/planned approach

This term basically refers to the criticisms mentioned in the last section and the recent focus on the use of confidence intervals and estimates
This is combined with the use of the modern maximum likelihood-based analysis and methods for reproducible research