## Calculating sample size for a 2 independent sample t-test

Scientists often plan for studies by calculating how many subjects or units need to be tested in order to find an effect. That is, they plan for a study using statistical power according to principles of hypothesis testing. Sample size calculations are usually required in ethics applications and grant proposals to justify the study. We previously learned how to calculate

## Cohen’s d: what standardiser to use?

In a previous post we learned about Cohen’s d, a standardized measure of effect size. In this post we will learn why it is important to consider what value is being used to standardize our effect size. Cohen’s d and the standardiser The basic formula to calculate Cohen’s d is: d = [effect size / relevant standard deviation]. The denominator

## Cohen’s d: a standardized measure of effect size

Various tools, scales and techniques are available to researchers to quantify outcome measures. Some of these tools are familiar, like a weight scale to measure weight loss over the course of an exercise program. Others are less familiar and are only understood by those working in the same field. Furthermore, different outcome measures can be calculated from the same data.

## Organize your project files and folders

I have completed undergraduate level research, a Master’s degree, a PhD, and two post-docs, yet I have never been taught or told how I should organize the files and folders related to a project. Being organized is essential in most aspects of life, and this is particularly trying when conducting research. I only found a few online examples of blogs