Development of Evidential Methodology for Analysis of Genetic Data

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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.