## 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.

## Independent t-test as a linear model in R

My last two posts have shown how to perform an independent t-test in the R programming language and the Python programming language. For those of you who are familiar with statistics, you likely know that an independent t-test is equivalent to performing an one-way analysis of variance on the data. What you may not have realised is that both these

## Independent t-test in Python

In a previous post we learned how to perform an independent t-test in R to determine whether a difference between two groups is important or significant. In this post we will learn how to perform the same test using the Python programming language. Along the way we will learn a few things about t distributions and calculating confidence intervals. dataset.In

## Independent t-test in R

As scientists, we often want to know if the difference between two groups is important or significant. For example, you may have data on leg strength from students who came to class wearing dress shoes or running shoes. How would you decide if there was a difference in strength between these two groups? How would you quantify the size of

## Research funding: let’s play the lottery!

Last year, Fang & Casadevall (2016) wrote an editorial entitled Research Funding: the Case for a Modified Lottery highlighting the chronic and severe lack of research funds and the resistance to change how these funds are allocated. While their proposal may seem drastic, especially to the lucky 10% or so who get their grants funded, the arguments put forth by