• # “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.

• # Sport-Specific or “Culture-Specific”?

Recently a friend of mine and a fellow physical preparation coach, who was working with futsal and was preparing Olympic level Judokas, got an offer to take care of a pro basketball team. Since I was the one recommending him to the agent, I was questioned would he be a good fit, taking into account his lack of experience…

• # Training Periodization, Sprinting, Tempo, Charlie Francis, Technology and Much More [Discussion]

This is the discussion that emerged on my Facebook wall after my post on Stephen Seiler presentation for INSEP regarding the MENAGEMENT OF THE DISTRIBUTION OF TRAINING INTENSITY: THE POLARIZED MODEL and my suggestion for similar observation and experiment to Mike Tuchscherer involving powerlifters instead of endurance runners.

• # 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.

• # How to Visualize Test Change Scores for Coaches

Coaches are not interested in making inferences to a population, statistical significance, nor mean values (P<0.01). What they are interested are individual athletes, their reactions to training and what are practically significant and meaningful changes.