Do you have any recommended books or resources by Taleb or another author on how to identify or avoid methodological flaws like p-hacking, understanding correlation vs causation, and more?
I'm currently taking a data science course on Udemy and learning about chi squares, regression, and decision trees, but I'd love more information on best practices especially for experimental design.
I'm currently taking a data science course on Udemy and learning about chi squares, regression, and decision trees, but I'd love more information on best practices especially for experimental design.