Abstract
Data binning can cope with overplotting and noise, making it a versatile tool for comparing many observations. However, it goes awry if the same observations are used for binning and contrasting. This creates an inherent circularity, leaving noise and regression to the mean insufficiently controlled. Here, we use population receptive field analyses – where data binning is commonplace – as an example to expose this flaw through simulations and empirical repeat data.
Competing Interest Statement
The authors have declared no competing interest.
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.