tions found in available data may not be uniform, as they would be in a tightly controlled experiment. The investigator would like follow the practice of clinical trials and report a single, primary analysis using everyone without multiplicity correction. Nonetheless, the investigator would like to speak to the possibility that there are versions of treatment or control conditions. The proposed method always reports two interval estimates. The first, shorter interval, I $_{c}$, is precisely the interval that would be reported in a single pri- mary analysis without versions of treatment. The second longer interval, I $_{v}$, attempts to cover both treatment effects if there are two versions of treatment or two versions of control. If there is, in fact, only a single treatment effect, the same for both versions, then the probability that both I c and I v simultaneously cover that one effect is the stated rate of 1 − α . If there are, in fact, two treatment effects that differ with the two versions, then the second interval, I $_{v}$, covers both effects with the stated rate of 1 − α . In that sense, the added information provided by reporting two intervals, I c and I $_{v}$, is free: the interval I c is clarified but not lengthened by examining I $_{v}$. Although I v is always somewhat longer than I $_{c}$, in the football example it is only slightly longer, thereby suggesting that the primary analysis is not greatly distorted by the two versions of the control condition. References Bachynski, K. E. (2016), “Tolerable risks? physicians and tackle football,” New England Journal of Medicine , 374, 405-407. Berger, R. L. (1982), “Multiparameter hypothesis testing and acceptance sampling,” Tech- nometrics , 24, 295-300. Campbell, D. T. (1969), “Prospective: Artifact and Control,” in R. Rosenthal and R. L. Rosnow, eds., Artifact in Behavioral Research , New York: Academic Press, 351-382. Conover, W. J. and Salsburg, D. S. (1988), “Locally most powerful tests for detecting treat- 23