Stand up to your Defense Panel with Small Non Random Samples
Power and Efficiency at high price Older science (and social science) students remember being told during their one applied statistics course, like “Statistics for Communication” or “Research Statistics” that parametric tests, such as t-tests and analysis of variance are powerful and more efficient in discriminating between true and false hypotheses than non parametric tests. In general, this is true. But the power and efficiency of parametric tests impose a priori stringent conditions: for examples, the level of measurement must be in interval or ratio scales; samples must be randomly selected, and they must be large enough [(or as statisticians love to call them, “asymptotic”); how large can be precisely calculated, provided you have an idea of “tolerable variance”. Some statisticians give 100 approximately, some 50, Walpole allows 30, BUT!!]; they must come from approximately normal populations. How do you know if the population is normal? Well, you must have made certain th...