world applications of hypothesis testing include: Testing whether more men than women suffer from nightmares Establishing authorship of documents Evaluating the effect of the full moon on behavior Determining the range at which a bat can detect an insect by echo Deciding whether hospital carpeting results in more infections Selecting the best means to stop smoking Checking whether bumper stickers reflect car owner behavior Testing the claims of handwriting analysts Statistical hypothesis testing plays an important role in the whole of statistics and in statistical inference. For example, Lehmann (1992) in a review of the fundamental paper by Neyman and Pearson (1933) says: "Nevertheless, despite their shortcomings, the new paradigm formulated in the 1933 paper, and the many developments carried out within its framework continue to play a central role in both the theory and practice of statistics and can be expected to do so in the foreseeable future". Significance testing has been the favored statistical tool in some experimental social sciences (over 90% of articles in the Journal of Applied Psychology during the early 1990s). Other fields have favored the estimation of parameters (e.g. effect size). Significance testing is used as a substitute for the traditional comparison of predicted value and experimental result at the core of the scientific method. When theory is only capable of predicting the sign of a relationship, a directional (one-sided) hypothesis test can be configured so that only a statistically significant result supports theory. This form of theory appraisal is the most heavily criticized application of hypothesis testing. Cautions "If the government required statistical procedures to carry warning labels like those on drugs, most inference methods would have long labels indeed." This caution applies to hypothesis tests and alternatives to them. The successful hypothesis test is associated with a probability and a type-I error rate. The conclusion might be wrong. The conclusion of the test is only as solid as the sample upon which it is based. The design of the experiment is critical. A number of unexpected effects have been observed including: The clever Hans effect. A horse appeared to be capable of doing simple arithmetic. The Hawthorne effect. Industrial workers were more productive in better illumination, and most productive in worse. The placebo effect. Pills with no medically active ingredients were remarkably effective. A statistical analysis of misleading data produces misleading conclusions. The issue of data quality can be more subtle. In