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 previous article in this series, we covered teamwork and networking. Now it’s time to move on to what many people consider the most boring part of the lab work: the analysis. I know we all wish that a simple histogram or a rather nice-looking Western blot or PCR would suffice. But the fact [...]

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 [...]