Development of Evidential Methodology for Analysis of Genetic Data

遗传学领域需要新的统计方法来处理日益增长的规模和复杂性的数据集。统计范式惩罚调查人员彻底询问他们的数据(如。多重假设检验问题)将不再受欢迎。证据范式（EP）是频率主义范式和贝叶斯范式的一个鲜为人知的替代方案，它使用两个简单假设的似然比（LR）来衡量给定数据体中的相对证据强度。通过LRs解释证据强度是可靠的，其中假设的假值很少比真值得到更好的支持，并且观察到有利于假值的证据的概率M可以通过样本量来控制。但是M并不影响对给定数据体中证据强度的解释，这是由LR表示的。由于这种错误概率和证据强度的解耦，我们已经表明，确实，由于评估多个假设，M会增加，但这可以通过复制研究来控制；这是遗传学领域的常规做法。

The genetics field is in need of novel statistical methodology to deal with data sets of growing size and complexity. Statistical paradigms that penalize investigators for thoroughly interrogating their data (ie. the multiple hypothesis testing problem) will fall out of favour. The evidential paradigm (EP), a less-known alternative to Frequentist and Bayesian paradigms, uses the likelihood ratio (LR) for two simple hypotheses to measure the relative evidence strength in a given body of data. Interpreting evidence strength via LRs is reliable, where false values for the hypotheses are rarely better supported than true values, and the probability of observing evidence favouring false values, M, can be controlled by the sample size. But M does not affect the interpretation of the evidence strength in a given body of data, which is represented by the LR. Due to this decoupling of error probabilities and evidence strength we have shown that, indeed, there is an increase in M due to assessing multiple hypotheses, but this can be controlled by a replication study; a routine practice in the genetics field.