Hi all, Our next meeting will be *Wednesday February 12*, where Adam Kapelner will present research on* “**Harmonizing Optimized Designs with Classic Randomization in Experiments**”. * *Abstract:* There is a long debate in experimental design between the classic randomization design of Fisher, Yates, Kempthorne, Cochran, and those who advocate deterministic assignments based on notions of optimality. In nonsequential trials comparing treatment and control, covariate measurements for each subject are known in advance, and subjects can be divided into two groups based on a criterion of imbalance. With the advent of modern computing, this partition can be made nearly perfectly balanced via numerical optimization, but these allocations are far from random. These perfect allocations may endanger estimation relative to classic randomization because unseen subject-specific characteristics can be highly imbalanced. To demonstrate this,we consider different performance criterions such as Efron’s worst-case analysis and our original tail criterion of mean squared error. Under our tail criterion for the differences-in-mean estimator, we prove asymptotically that the optimal design must be more random than perfect balance but is less random than completely random. Our result vindicates restricted designs that are used regularly such as blocking and rerandomization. For a covariate-adjusted estimator, balancing offers less rewards and it seems good performance is achievable with complete randomization. Further work will provide a procedure to find the explicit optimal design in different scenarios in practice. Supplementary materials for this article are available online. The paper can be found here <https://amstat.tandfonline.com/doi/abs/10.1080/00031305.2020.1717619#.Xj1kflVKhhE> . *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, February 12 at 12 noon - 1:30 pm. As always, all are welcome and lunch will be provided. Best, Georgie