Comparing one-step m-estimators of location corresponding to two independent groups

Experience with real data indicates that psychometric measures often have heavy-tailed distributions. This is known to be a serious problem when comparing the means of two independent groups because heavy-tailed distributions can have a serious effect on power. Another problem that is common in some areas is outliers. This paper suggests an approach to these problems based on the one-step M-estimator of location. Simulations indicate that the new procedure provides very good control over the probability of a Type I error even when distributions are skewed, have different shapes, and the variances are unequal. Moreover, the new procedure has considerably more power than Welch's method when distributions have heavy tails, and it compares well to Yuen's method for comparing trimmed means. Wilcox's median procedure has about the same power as the proposed procedure, but Wilcox's method is based on a statistic that has a finite sample breakdown point of only 1/n, wheren is the sample size. Comments on other methods for comparing groups are also included.     

Data Trimming, Nuclear Emissions, and Climate Change

Ethics requires good science. Many scientists, government leaders, and industry representatives support tripling of global-nuclear-energy capacity on the grounds that nuclear fission is “carbon free” and “releases no greenhouse gases.” However, such claims are scientifically questionable (and thus likely to lead to ethically questionable energy choices) for at least 3 reasons. (i) They rely on trimming the data on nuclear greenhouse-gas emissions (GHGE), perhaps in part because flawed Kyoto Protocol conventions require no full nuclear-fuel-cycle assessment of carbon content. (ii) They underestimate nuclear-fuel-cycle releases by erroneously assuming that mostly high-grade uranium ore, with much lower emissions, is used. (iii) They inconsistently compare nuclear-related GHGE only to those from fossil fuels, rather than to those from the best GHG-avoiding energy technologies. Once scientists take account of (i)–(iii), it is possible to show that although the nuclear fuel cycle releases (per kWh) much fewer GHG than coal and oil, nevertheless it releases far more GHG than wind and solar-photovoltaic. Although there may be other, ethical, reasons to support nuclear tripling, reducing or avoiding GHG does not appear to be one of them.