What Is There

The mindless use of null-hypothesis significance testing – the significance test ritual (e.g., Salsburg, 1985) – has long been criticized. The main component of the ritual can be characterized as follows: Once you have collected your data, try to refute your null hypothesis (e.g., no mean difference, zero correlation, etc.) in an automatized manner. Often the ritual is complemented by the “star procedure”: If p < . 05, assign one star to your results (*), if p < . 01 give two stars (**), and if p < . 001 you have earned yourself three stars (***). If you have obtained at least one star, the ritual has been successfully performed; if not, your results are not worth much. The stars, or the corresponding numerical values, have been door-openers to prestigious psychology journals and, therefore, the ritual has received strong reinforcement. The ritual does not have a firm theoretical grounding; it seems to have arisen as a badly understood hybrid mixture of the approaches of Ronald A. Fisher, Jerzy Neyman, Egon S. Pearson, and (at least in some variations of the ritual) Thomas Bayes (see Acree, 1979; Gigerenzer & Murray, 1987; Spielman, 1974). For quite some time, there has been controversy over its usefulness. The debates arising from this controversy, however, have not been limited to discussions about the mindless procedure as sketched above, but have expanded to include the issues of experimental design and sampling procedures, assumptions about the size of population effects (leading to the specification of an alternative hypothesis), deliberations about statistical power before the data are collected, and decisions about Type I and Type II errors. There have been several such debates and the controversy is ongoing (for a summary see Balluerka, Gomez, & Hidalgo, 2005; Nickerson, 2000; Sedlmeier, 1999, Appendix C). Although there have been voices that argue for a ban on significance testing (e.g., Hunter, 1997), authors usually conclude that significance tests, if conducted properly, probably have some value (or at least do no harm) but should be complemented (or replaced) by other more informative ways of analyzing data (e.g., Abelson, 1995; Cohen, 1994; Howard, Maxwell, & Fleming, 2000; Loftus, 1993; Nickerson, 2000; Sedlmeier, 1996; Wilkinson & Task Force on Statistical Inference, 1999). Alternative data-analysis techniques have been wellknown among methodologists for decades but this knowledge, mainly collected in methods journals, seems to have had little impact on the practice of researchers to date. I see two main reasons for this unsatisfactory state of affairs. First, it appears that there is still a fair amount of misunderstanding about what the results of significance tests really mean (e.g., Gordon, 2001; Haller & Krauss, 2002; Mittag & Thompson, 2000; Monterde-i-Bort, Pascual Llobell, & Frias-Navarro, 2008). Second, although alternatives have been briefly mentioned in widely received summary articles (such as Wilkinson & Task Force on Statistical Inference, 1999), they have rarely been presented in a nontechnical and detailed manner to a nonspecialized audience. Thus, researchers might, in principle, be willing to change how they analyze data but the effort needed to learn about alternative methods might just be regarded as too great. The main aim of this special issue is to introduce a collection of these alternative data-analysis methods in a nontechnical way, described by experts in the field. Before introducing the contents of the special issue, I will briefly outline the ideal state of affairs in inference statistics and discuss the difference between mindless and mindful significance testing.