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Most of us in the biotech space are fully onboard with the concept of reproducible research. Who thinks it’s a bad idea to be able to trace back from our results to the data and methods that produced them? The trick, of course, is in how to do it. In the lab, there’s a strong… Read More »The Compiled Thesis: why computational biologists should use self-documented analysis

I’ve been asked before to explain how repeated measurements can impact statistical models. We often bake repeated measurements – more than one measurement take on the same person, the same experimental animal, etc – into our experiments. They can give us more confidence in noisy data. The downside is that they do need to be… Read More »The Sketchy Data Collector and the Problem with Repeated Measurements

What on earth do people mean when they tell you they’re going to “fit a model”? Let’s start with what a model is. A model is a description of a system, usually expressed as an equation of some kind. Let’s say we have some data – measurements of variables x and y. We think that… Read More »What does it mean to fit a model, anyway?

Last week was the annual BioIT World Convention and Expo. It’s three days of talks, workshops, and row upon row of technology solutions vendors putting their best foot forward. Also, biotech folks from all over the country converge upon Boston, so I get to catch up with many colleagues I see but once a year.… Read More »BioIT World 2017

The terms probability and likelihood are often used interchangeably in day-to-day conversation. They have specific meanings in the world of statistics, however, and understanding the difference is helpful in understanding statistical methods. We’ll use examples to start. Take a coin flip: if you flip a coin, and you know it’s fair; a lifetime of experience… Read More »Probability vs Likelihood: What’s the Difference?

Version control is not just for software engineers – computational scientists can see huge benefit from version control features.