Model selection is the process of selecting one statistical model from a set of many candidate models. It seems relatively straightforward in principal but in practice (especially with noisy experimental data) it can be challenging.
If you’ve ever handled data you’ve probably faced the ever daunting first step of cleaning and tidying said data. As a matter of fact, it’s been mentioned that 80% of data analysis is spent solely on the process of cleaning and preparing the data [1].
People have studied growth in a multitude of organisms from unicellular models like bacteria and yeast, to animal models like drosophila and mice. To assess what developmental growth looks like for any one of these organisms is relatively straightforward; the more challenging — and inherently more interesting — question lies in HOW is growth regulated.