When researchers suspect that error terms are correlated by group in observational research the standard correction is to cluster the standard errors. But what about in experimental contexts where treatment is randomised? Despite their ubiquity in analyses with group-constant variables, the rationale for using clustered standard errors in experimental contexts remains underdeveloped. In this paper I present an intuitive and applied explanation of when clustering is appropriate, building on recent contributions in the statistics and econometrics literatures. I demonstrate why randomisation does not lead to identical variance estimates across estimation strategies, and conduct a review of experimental studies published between 2017 and 2019 to show that these differences can be considerable. Finally, I provide practical guidance for when and why to cluster standard errors for common experimental designs.