Sarah-Jane O'Connor
Sarah-Jane is an ecologist who sometimes masquerades as a geneticist. Her statistical knowledge is embarassing in some social circles, but revered in others. Which probably just makes it neutral. She has a PhD in ecology from the University of Canterbury, NZ.
Articles by Sarah-Jane O'Connor:
BioNumbers: An Online Database of Useful Biological Numbers
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?!…
What Can Mendeley Do For You?
Sometimes it feels like all we do, as scientists, is read other people’s work. In which case, it’s not surprising that the first software that was impressed upon me as a new postgraduate student was for reference management. At my university, we are encouraged to use EndNote, so this is what I started on. A…
Time for T: How to Use the Student’s T-test
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…
Pseudoreplication: Don’t Fall For This Simple Statistical Mistake
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…
How Free is Your Degree?
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…
Don’t Be Another P-value Victim
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…
Types of Statistical Errors and What They Mean
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…
A Primer on Statistical Hypotheses
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…
Why You Should Care More About Statistics
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…
