Sarah-Jane O'Connor's Profile

read on

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?! […]

read on
In Lab Statistics & Math 7th of December, 2012
read on

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 […]

read on
In Taming the Literature 1st of June, 2012
read on

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 […]

read on
In Lab Statistics & Math 25th of January, 2012
read on

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 […]

read on
In Lab Statistics & Math 23rd of January, 2012
read on

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 […]

read on
In Lab Statistics & Math 18th of January, 2012
read on

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 […]

read on
In Lab Statistics & Math 16th of January, 2012
read on

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 […]

read on
In Lab Statistics & Math 13th of October, 2011
read on

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 […]

read on
In Lab Statistics & Math 12th of October, 2011
read on

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 […]

read on
In Lab Statistics & Math 12th of September, 2011