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Kristin Sainani has a useful statistics column in PM&R.
This ‘Reference Collection to push back against “Common Statistical Myths”’ is very useful for educational purposes, for myth-busting, and also during the publication and peer-review process.
Joshua Starmer has an excellent StatQuest YouTube channel which breaks down statistics and machine learning methods into short animated videos that are easy to understand.
Similarly, although slightly longer, Andy Field posted a series of Undergraduate statistics lectures on YouTube.
How to justify an alpha (not necessarily 0.05)— Stuart McErlain-Naylor (@biomechstu) June 14, 2021
A really well written preprint by @MaxMa1er and @lakens: https://t.co/MhPMD8cVWk
Did you know, with high sample sizes certain p values below alpha can be more likely under a true null hypothesis than under a true alternative hyp? pic.twitter.com/SAs6VFyLHe
This editorial in Journal of Sports Sciences, titled ‘Power, precision, and sample size estimation in sport and exercise science research’ should be useful across all disciplines.
G*Power is a free and easy to use tool for power analysis. For a short (under 5 min) demonstration, see my tutorial video below:
Considerations such as resource constraints are also important when planning a study / sample size - as discussed in this pre-print by Daniel Lakens.
If you have to deal with small samples due to various constraints, then this paper provides recommendations for how best to do so.
The Twitter thread below walks you through the use of simulated data to determine power and other characteristics in R:
Here is a little intro thread on how to do simulations of randomized controlled trials.— Andrew Althouse (@ADAlthousePhD) January 25, 2021
This thread will take awhile to get all the way through & posted, so please be patient. Maybe wait a few minutes and then come back to it.
Many power analyses I read in published manuscripts are not reported with sufficient transparency to understand what on earth they are doing! What is the effect estimate? Where did it come from? For what test does it apply? Etc. Here is a reporting guide I give students#phdchat pic.twitter.com/BHPThzaiZJ— Guy Prochilo 🏳️ 🌈 (@GuyProchilo) August 10, 2020
Interested in performing power analysis but not sure where to start? Check out the "jpower" module in @jamovistats, from @richarddmorey.— Dan Quintana (@dsquintana) May 19, 2019
The way this module is structured is an excellent introduction to power analysis, here's how it works... pic.twitter.com/knBy78PrGx
For an introduction to Bayesian inferential statistics, I recommend the paper Bayesian data analysis for newcomers.
Tony Myers gave a lecture on ‘Bayesian Statistics for Sport Science’ as part of my Sports Biomechanics Lecture Series:
I presented ‘a practical open-source comparison of discrete and continuous biomechanical analysis techniques’ at the International Society of Biomechanics in Sports conference in 2020 (video below; paper here ):