Resampling and simulation techniques
Overview
Traditional statistical testing make use of various distributions and assumptions. This often works well, allowing us to analyse our data using standardised tests - or slight variations on them.
However, sometimes you might end up with data that are just weird and the standard tests or even the adaptations of them no longer work. This is where resampling and simulation techniques are very useful. Here we use either the original data or simulate new data to explore our hypothesis.
These topics rely on a mixture of statistical literacy and programming competencies. These materials are aimed to provide background and practical tools to address this.
Note: The materials are under active development. The learning objectives in brackets will be addressed in further versions of these materials.
- Understand which resampling techniques there are and when to use them.
- Analyse data through permutation techniques
- (Bootstrapping)
- (Cross-validation)
- (Simulation data)
Target audience
This course is aimed at researchers and data analysts with an intermediate level of statistical and programming knowledge.
Prerequisites
Confident in the use of R / Python; basic knowledge of statistics (e.g. attended the Core statistics course).
Exercises
Exercises in these materials are labelled according to their level of difficulty:
| Level | Description |
|---|---|
| Exercises in level 1 are simpler and designed to get you familiar with the concepts and syntax covered in the course. | |
| Exercises in level 2 combine different concepts together and apply it to a given task. | |
| Exercises in level 3 require going beyond the concepts and syntax introduced to solve new problems. |