Ever wondered how to deal with confounds and covariates in experimental design? A new blogpost explaining these concepts and collecting some advice: Also featuring causal graphical models and Bob Dylan!
Not quite sure what ‘confounds’ and ‘covariates’ are? If you care about correlation and causation, you should know—and thanks to this super post (introducing the “Bob Dylan hypothesis”) by you can: HT
Not quite sure what ‘confounds’ and ‘covariates’ are? If you care about correlation and causation, you should know—and thanks to this super post (introducing the “Bob Dylan hypothesis”) by you can: HT