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Assessing environmentally significant effects: a better strength-of-evidence than a single P value?

机译:评估对环境的重大影响:证据强度比单个P值好吗?

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Interpreting a P value from a traditional nil hypothesis test as a strength-of-evidence for the existence of an environmentally important difference between two populations of continuous variables (e.g. a chemical concentration) has become commonplace. Yet, there is substantial literature, in many disciplines, that faults this practice. In particular, the hypothesis tested is virtually guaranteed to be false, with the result that P depends far too heavily on the number of samples collected (the 'sample size'). The end result is a swinging burden-of-proof (permissive at low sample size but precautionary at large sample size). We propose that these tests be reinterpreted as direction detectors (as has been proposed by others, starting from 1960) and that the test's procedure be performed simultaneously with two types of equivalence tests (one testing that the difference that does exist is contained within an interval of indifference, the other testing that it is beyond that interval-also known as bioequivalence testing). This gives rise to a strength-of-evidence procedure that lends itself to a simple confidence interval interpretation. It is accompanied by a strength-of-evidence matrix that has many desirable features: not only a strong/moderate/ dubious/weak categorisation of the results, but also recommendations about the desirability of collecting further data to strengthen findings.
机译:从传统的零假设检验中将P值解释为两个连续变量种群(例如化学浓度)之间在环境上具有重要意义的差异的证据强度已变得司空见惯。但是,在许多学科中,有大量文献误导了这种做法。特别是,实际上可以保证所检验的假设是错误的,结果是P很大程度上取决于收集的样本数量(“样本量”)。最终的结果是摇摆的举证责任(在小样本量时是允许的,但在大样本量时是预防性的)。我们建议将这些测试重新解释为方向检测器(正如其他人从1960年开始提出的那样),并且该测试的过程应同时进行两种等效测试(一种测试是在一个区间内包含确实存在的差异)冷漠,另一项超出此间隔的测试(也称为生物等效性测试)。这就产生了一种证据强度程序,使其易于进行简单的置信区间解释。它伴随着证据强度矩阵,该矩阵具有许多理想的功能:不仅对结果进行强/中等/可疑/弱分类,而且还建议收集更多数据以加强发现。

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