Lab Statistics & Math

So now you’re convinced that R is the language for you, you’ve downloaded R-Studio (from http://www.rstudio.com/)  and opened it, and. . .what the hell do you do now? Great question! I always find it easiest to learn by doing something, rather than just by seeing a list of possibilities, so here I’ll walk you through…

Working with large datasets can be very frustrating and time consuming.  If only there were more tools out there to simplify things without needing to invest a PhD’s worth of time to learn how to use them! I am here to tell you that there is a solution, and a free one at that. If…

Who wants to know how many cells are in a single colony of Escherichia coli? (3.39). Or the egg size of Drosophila melanogaster? (12.3 nL). Or how about the genomic mutation rate in Arabidopsis thaliana? (0.28 – 0.42 mutations per diploid genome per generation). Who am I kidding? Who wouldn’t want to know those numbers?!…

Like many scientists, I don’t consider myself a statistics expert. But I am determined to do things right in my science, and that includes statistics. In my experience, a lot of scientists who are “scared” of statistics fall into the trap of ignoring the existence of anything beyond a t-test. But using the right method…

To pull together our discussions so far on hypothesis testing and p-values, we will use the t distribution as an example to see how it all works. The t distribution (you may have heard it called Student’s t) is a probability distribution that looks like a bell-shaped curve (or normal distribution). If we sample repeatedly from…

Now we come to the third part of our trifecta; in the last two posts I have gone over p-values and how they determine significance in null hypothesis testing, and we talked about degrees of freedom and their effect on the p-value. Finally, we come to pseudoreplication: where it can all go terribly wrong. Replication…

In the last post I talked about p-values and how we define significance in null hypothesis testing. P-values are inherently linked to degrees of freedom; a lack of knowledge about degrees of freedom invariably leads to poor experimental design, mistaken statistical tests and awkward questions from peer reviewers or conference attendees. Even if you think…

In previous articles, I’ve primed you on hypothesis testing and how we are forced to choose between minimising either Type I or Type II errors. In the world of the null hypothesis fetish, the p-value (p) is the most revered number. It may also be the least understood. The p-value is the probability, assuming the…

This column is loaded with pop quizzes for you to test yourself on. If you haven’t already done so, catch up on yesterday’s piece on hypothesis testing for a refresher. Take a gander at the table below for a summary of the two types of error that can result from hypothesis testing. Type I Errors occur…

Hypothesis testing is the foundation around which we prove our science is worth funding, publishing and sitting through a conference presentation for. I can’t overstate the  importance of understanding hypothesis testing, such is the integral part it plays in biological analyses. The Null Hypothesis Fundamental to statistics is the concept of a null hypothesis, and…

In this, the first in a series of articles on statistics, I want to set out some of the main reasons why you, as a biologist, should improve your knowledge of statistics. The general consensus is that biologists are not strong when it comes to statistics. There’s nothing in our brains that stops us from…

Greg Petsko, President of the American Society for Biochemistry wrote a very interesting article recently in which he drew attention to the parallels between the PhD/Postdoc system and the medieval trade guilds, and the problems our profession faces because it is drifting away from that system. In the trade guild system the right of an…

….statistics. The very word strikes fear into the heart of many a biologist (including me). In an article published earlier this year, Cumming and co-workers of La Trobe University, Melbourne gave a very useful rundown of common mistakes made when using statistical error bars in biology and suggested a number of rules that should be…