INTRODUCTION
Simulation is a powerful tool in the data science tool box. After reading this article, you’ll have a good understanding of how simulation can be used to estimate the power of a designed experiment. This is the second part of a multi-part series that discusses how simulation can be useful in data science and machine learning.
Here are the contents that we’ll cover:
- Overview of power analysis
- How to calculate power using simulation — example based approach
In this article, I will just give a quick definition of data simulation:
Data simulation is the creation of fictitious data that mimics the properties of the real-world.
In part 1 of this series, I discuss the definition of data simulation much more extensively — you can check it out at the link below:
OVERVIEW OF POWER ANALYSIS