Most power calculators take a small number of inputs: sample size, effect size, and variance. Some also allow for number of blocks or cluster size as well as the overall sample size. All of these inputs relate to your data strategy. Unless you can control the effect size and the noise, you are left with sample size and data structure (blocks and clusters) as the only levers to play with to try to improve your power.

You can often improve the precision of your randomized controlled trial with blocking: first gather similar units together into groups, then run experiments inside each little group, then average results across experiments. Block random assignment (sometimes called stratified random assignment) can be great—increasing precision with blocking is like getting extra sample size for free. Blocking works because it’s like controlling for a pre-treatment covariate in the “Data Strategy” rather than in the “Answer Strategy.” But sometimes it does more harm than good.

A dangerous fact: it is quite possible to talk in a seemingly coherent way about strategies to answer a research question without ever properly specifying what the research question is. The risk is that you end up with the right solution to the wrong problem. The problem is particularly acute for studies where there are risks of “spillovers.”

Consider an observational study looking at the effect of a non-randomly assigned treatment, \(Z\), on an outcome \(Y\). Say you have a pretreatment covariate, \(X\), that is correlated with both \(Z\) and \(Y\). Should you control for \(X\) when you try to assess the effect of \(Z\) on \(Y\)?

Welcome to the `DeclareDesign`

blog! We have been working on developing the `DeclareDesign`

family of software packages to let researchers easily generate research designs and assess their properties. Our plan over the next six months is to put up weekly blog posts showing off features of the packages or highlighting the kinds of things you can learn about research design using this approach.