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【正文】 a significant result are reported. Those making critical decisions based on the results of a hypothesis test are prudent to look at the details rather than the conclusion alone. In the physical sciences most results are fully accepted only when independently confirmed. The general advice concerning statistics is, Figures never lie, but liars figure (anonymous). Controversy Since significance tests were first popularized many objections have been voiced by prominent and respected statisticians. The volume of criticism and rebuttal has filled books with language seldom used in the scholarly debate of a dry subject. Much of the criticism was published more than 40 years ago. The fires of controversy have burned hottest in the field of experimental psychology. Nickerson surveyed the issues in the year 2021. He included 300 references and reported 20 criticisms and almost as many remendations, alternatives and supplements. The following section greatly condenses Nickerson39。s t distribution or a normal distribution. 6. Select a significance level (α), a probability threshold below which the null hypothesis will be rejected. Common values are 5% and 1%. 7. The distribution of the test statistic under the null hypothesis partitions the possible values of T into those for which the nullhypothesis is rejected, the so called critical region, and those for which it is not. The probability of the critical region is α. 8. Compute from the observations the observed value tobs of the test statistic T. 9. Decide to either fail to reject the null hypothesis or reject it in favor of the alternative. The decision rule is to reject the null hypothesis H0 if the observed value tobs is in the critical region, and to accept or fail to reject the hypothesis otherwise. Use and Importance Statistics are helpful in analyzing most collections of data. This is equally true of hypothesis testing which can justify conclusions even when no scientific theory exists. Real world applications of hypothesis testing include [7]: ? 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 shortings, 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).[8] Other fields have favored the estimation of parameters. Editors often consider significance as a criterion for the publication of scientific conclusions based on experiments with statistical results. Cautions The successful hypothesis test is associated with a probability and a typeI error rate. The conclusion might be wrong. 濟(jì)南大學(xué) 泉城學(xué)院 畢業(yè)論文 外文資料翻譯 4 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: ?
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