#Sigmaplot 11 do not test bonferroni how to#

Evaluating replicability of laboratory experiments in economics. F., Dreber, A., Forsell, E., Ho, T.-H., Huber, J., Johannesson, M., et al. Technical report, cemmap working paper, Centre for Microdata Methods and Practice.Ĭamerer, C. Inference under covariate-adaptive randomization. Rome: Tipografia del Senato.īugni, F., Canay, I., & Shaikh, A. Il calcolo delle assicurazioni su gruppi di teste. Treatment effect bounds: An application to swan-ganz catheterization. Strategic Management Journal, 33(1), 108–113.īhattacharya, J., Shaikh, A. The search for asterisks: Compromised statistical tests and flawed theories. Journal of the American Statistical Association, 103(484), 1481–1495.īettis, R. Multiple inference and gender differences in the effects of early intervention: A re-evaluation of the abecedarian, perry preschool, and early training projects.

We illustrate our methodology by revisiting the study by Karlan and List (Am Econ Rev 97(5):1774–1793, 2007) of why people give to charitable causes.Īnderson, M.

#Sigmaplot 11 do not test bonferroni how to#

In the presence of multiple treatments, we additionally show how to exploit logical restrictions across null hypotheses to further improve power. Importantly, by incorporating information about dependence ignored in classical multiple testing procedures, such as the Bonferroni and Holm corrections, our procedure has much greater ability to detect truly false null hypotheses. Using the general results in Romano and Wolf (Ann Stat 38:598–633, 2010), we show under weak assumptions that our procedure (1) asymptotically controls the familywise error rate-the probability of one or more false rejections-and (2) is asymptotically balanced in that the marginal probability of rejecting any true null hypothesis is approximately equal in large samples. In this paper, we provide a bootstrap-based procedure for testing these null hypotheses simultaneously using experimental data in which simple random sampling is used to assign treatment status to units. These different null hypotheses arise naturally in this setting for at least three different reasons: when there are multiple outcomes of interest and it is desired to determine on which of these outcomes a treatment has an effect when the effect of a treatment may be heterogeneous in that it varies across subgroups defined by observed characteristics and it is desired to determine for which of these subgroups a treatment has an effect and finally when there are multiple treatments of interest and it is desired to determine which treatments have an effect relative to either the control or relative to each of the other treatments. The analysis of data from experiments in economics routinely involves testing multiple null hypotheses simultaneously.