• # Spider Charts Sucks!

Spider Charts Sucks! I am openly sharing my opinion (backed up with work by Stephen Few) that spider charts sucks. It is time for the sport scientists to move away from PES (Pro Evolution Soccer; game on Sony Playstation) and similar games influencing how we visualize athlete profiles. Let’s assume we have a movement screen scores and we want...

• # How to Analyze Movement Screen Tests?

How to Analyze Movement Screen Tests? In the recent post I shared the movement screen we designed and implemented. The question now is how to analyze the data and make real life decisions on it? What do we need to get from the analysis in the first place? In my (current) opinion we need these: Identify groups of athletes...

• # Continuing with Statistical Power simulation in R

In the last blog post I created a simple simulation of statistical power (probability to identify effects when they are really there) calulation depending on the sample size and effect size (Cohen’s D using Will Hopkins effect levels).

• # “Power” to detect statistically significant effects based on sample size and magnitudes of effects

I was going through magnitude-based inferences materials by Will Hopkins and I am playing with R simulations. I wanted to see how many times I am able to detect statistically significant effects (p<0.05) depending on magnitude of effects (expressed as Cohen's D, and using Will Hopkins levels) and sample sizes.

• # Playbook: Understanding MODERATION Through Simulation

Playbook: Understanding MODERATION Through Simulation Introduction I recently spoke with my college professor regarding the understanding of statistics, and I remarked that I learn the statistical concept the best (and with full comprehension) through simulation. He remarked that I might be in the minority of students (p<0.01 – see what I did here?). Not sure if this is true...

• # How to Visualize Test Change Scores for Coaches [Part 2]

To continue the previous How to visualize test change scores for coaches blogpost, here is another way to visualize individual change scores without falling for group averages. This way we can easily see individual ranks in both pre- and post- test, along with change score (which is also color coded). Quite easy to identify the outliers.

• # Understanding Inferential Statistics Using Correlation Example

In the following R and knitr experiment/blog post I will be documenting my play with correlation and inferences. I am just reading Discovering Statistics Using R by Andy Field and I am trying to code some staff from the book, plus experiment and see how inferential statistics work.

• # Quick correction in simulating Typical Error

I am awaiting review and opinions/critiques from statistics wizard Will Hopkins for my How to visualize test change scores for coaches, but even on his first look he pointed to one small error I did in simulating typical error for vertical jump. It is not huge error, but it is an error in representing typical error in measurement.