Now for the main part of the newsletter…
The month began with April Fools’ Day and a couple of humerous articles. Firstly, this proposed method
to streamline p-hacking (a random generator of p-values < 0.05) and then this paper
claiming to have generated all possible hypotheses (and requesting citations in all future hypothesis testing studies).
Periodisation for Mental Health
John Harry’s ‘Fun Friday’ blog post
applied the training concept of periodisation to academic workload management. I hadn’t thought about it this way very much but it seems to be a more formal representation of the approach I’ve adopted recently - periodisation sums it up brilliantly.
Search Engine Optimisation for Papers
I’ve done some reading around search engine optimisation recently, mostly for blog posts or YouTube videos - but then I discovered this article
on how to write a scientific article for Google / Google Scholar to read and prioritise. The main take-away points were that the main concept (the one people should search for) should be the first part of the title, you should include “X is [simple definition]” in the abstract, and your first image may be used as a preview. I guess there is a trade-off between titles that will receive the most attention in the short-term (possibly including puns or eye-catching titles) and those that will appear on searches for years to come. Related to this, my Twitter poll
revealed that most people use the topic as their title even though they themselves are most likely to click on and open articles with the answer/findings as the title. Apparently
hyphens in titles can also reduce citations, although I’m hoping that bug has since been fixed (for the sake of my surname if nothing else!).
What is a Markov Chain?
This Twitter thread
explains the concept of Markov Chains in an easy-to-follow step-by-step approach, with the example of two participants tossing coins and racing to their respective goal outcomes.
The Science of Scientific Writing
This one’s a listen rather than a read, but I really enjoyed Rob Gray’s Perception & Action Podcast episode
on white/brown/pink noise and why the standard deviation might not reflect variability in motor control. It’s a must-listen for anybody researching or interested in movement variability.
A useful editorial
in Sports Biomechanics by Richter et al. on the challenges and opportunities of machine learning in sports science. This provides a useful overview for anybody reading machine learning articles and wanting to understand or critically evaluate the methods used.
Sample size estimation for biomechanical waveforms
A really useful paper
(and Twitter thread
) for anybody conducting 1D biomechanical analyses. The explanatory diagrams throughout are a big help too. Interestingly, the 1D sample sizes required were always greater than for 0D (discrete values, peaks, etc.) analyses (from n + 1 to more than n + 20). For more on power analysis
or 1D analysis
you can check out my recent tutorial videos, or Todd Pataky’s continuous biomechanical data analysis lecture
This month was one year since Bill Baltzopoulos’ excellent lecture
on inverse dynamics terminology and mechanical misconceptions. This reminded me of the related paper
in which he explains the difference between an ‘Actual Forces’ approach and a ‘Resultant Moments’ approach to inverse dynamics, with their corresponding terminology and implications.