Spectroscopy is not only an important part of a scientist’s arsenal, but also a big part of everyday life. Take a moment to appreciate that the myriads of colours in dyes and paints are made possible by chemical compounds that possess certain spectral characteristics, or how the chemical composition of distant galaxies can be characterised based on their observed spectra. In this tutorial we will be discussing how infrared spectroscopy works, and its applications.

# Category Archives: Tutorials

# How to Get on the Road to Research

As I approached the completion of my undergraduate degree, I was definitely unsure of how research worked and the expectations that were required of me going forward. This resulted in a bit of confusion as I learned the ropes and how to handle my newfound freedom as I set out to do some proper research.

Looking back, I think what would have benefited me greatly was some simple guidance as I transitioned from routine, scheduled lectures to the erratic and unpredictable world of research. Guidance such as this feature article co-written with a fantastic collaborator.

# How to: Statistics – ANOVA

One thing that regularly stumps scientists is the handling of data. We seem to be very good at generating obscene amounts of it, but representing it meaningfully can be a little off putting if you don’t happen to be a bioinformatician. In previous tutorials we looked at hypothesis testing using variations of the t-Test, and we continue the series by comparing more than 2 samples sets with ANOVA.

# How To: Statistics – Two Sample and Paired t-Tests

One thing that regularly stumps scientists is the handling of data. We seem to be very good at generating obscene amounts of it, but representing it meaningfully can be a little off putting if you don’t happen to be a bioinformatician. Let’s continue our tutorial series by introducing Two-Sample t-Tests and Paired t-Tests to see how we can easily incorporate statistical analysis into our work.

# How To: Statistics – One Sample t-Tests

One thing that regularly stumps scientists is the handling of data. We seem to be very good at generating obscene amounts of it, but representing it meaningfully can be a little off putting if you don’t happen to be a bioinformatician. Let’s wet our toes with a simple One Sample t-test to see how we can easily incorporate statistical analysis into our work.

# How To: Graphs

Virtually all scientific reports and articles deal with numbers of some sort, and what better way to represent large amounts of data than on a well made graph? Unfortunately not all graphs are created equal; this post will introduce the basics of graph building and common pitfalls to avoid.