You won't be surprised that I eagerly watch James Hoffmann's videos. Especially the "if you were to plan some small experiment on your own - WITH COFFEE!" videos are really good.
So his new test of the "delay your morning #coffee" hypothesis was right down my street!
https://www.youtube.com/watch?v=yCJr49GU9yY
#HubermanLab
One thing I was wondering and which was not discussed in the comments I managed to read:
Were the data analysed in a way that took nesting / #RepeatedMeasures into account?
New tutorial paper describing the use of a simple #NonParametric #statistics method for analysing #RepeatedMeasures data with a focus on individual-level results https://pubs.asha.org/doi/10.1044/2022_JSLHR-22-00133
All data and #Rstats code needed to reproduce the analyses is available here https://osf.io/w32dk/
An #Rstats package which implements the method is available on CRAN https://CRAN.R-project.org/package=opa
The latest development version can be downloaded from githib https://github.com/timbeechey/opa
Purpose: This article provides a tutorial introduction to ordinal pattern analysis, a statistical analysis method designed to quantify the extent to which hypotheses of relative change across experimental conditions match observed data at the level of individuals. This method may be a useful addition to familiar parametric statistical methods including repeated measures analysis of variance and generalized linear mixed-effects models, particularly when analyzing inherently individual characteristics, such as perceptual processes, and where experimental effects are usefully modeled in relative rather than absolute terms.
A little while back I wrote an #rstats package for #NonParametric analysis of #RepeatedMeasures data https://CRAN.R-project.org/package=opa using #ordinal pattern analysis.
A tutorial with #rstats code is available here: https://osf.io/w32dk/
Jan R. Boehnke
Timothy Beechey