Deaton and Cartwright (2017) provide multiple arguments against claims that randomized trials should be thought of as a kind of gold standard of scientific evidence. One striking argument they make is that randomization does not justify the statistical tests that researchers typically use. They are right in that. Even if researchers can claim that their estimates of uncertainty are justified by randomization, their habitual use of those estimates to conduct *t*-tests are not. To get a handle on how severe the problem is we replicate the results in Deaton and Cartwright (2017) and then use a wider set of diagnosands to probe more deeply. Our investigation suggests that what at first seems like a big problem might not in fact be so great if your hypotheses are what they often are for experimentalists—sharp and sample-focused.

Spillovers are often seen as a nuisance that lead researchers into error when estimating effects of interest. In a previous post, we discussed sampling strategies to reduce these risks. A more substantively satisfying approach is to try to study spillovers directly. If we do it right we can remove errors in our estimation of primary quantities of interest and learn about how spillovers work at the same time.

The humble \(p\)-value is much maligned and terribly misunderstood. The problem is that everyone wants to know the answer to the question: “what is the probability that [hypothesis] is true?” But \(p\) answers a different (and not terribly useful) question: “how (un)surprising is this evidence given [hypothesis]?” Can \(p\) shed insight on the question we really care about? Maybe, though there are dangers.

Random assignment provides a justification not just for estimates of effects but also for estimates of uncertainty about effects. The basic approach, due to Neyman, is to estimate the variance in estimates of the difference between outcomes in treatment and in control outcomes using the variability that can be observed among units in control and units in treatment. It’s an ingenious approach and dispenses with the need to make any assumptions about the shape of statistical distributions or about asymptotics. The problem though is that it can sometimes be **upwardly biased**, meaning that it might lead you to maintain null hypotheses when you should be rejecting them. We use design diagnosis to get a handle on how great this problem is and how it matters for different estimands.

In many experiments, random assignment is performed at the level of clusters. Researchers are conscious that in such cases they cannot rely on the usual standard errors and they should take account of this feature by clustering their standard errors. Another, more subtle, risk in such designs is that if clusters are of different sizes, clustering can actually introduce bias, *even if all clusters are assigned to treatment with the same probability*. Luckily, there is a relatively simple fix that you can implement at the design stage.