Reviewers of research reports frequently criticize the choice of statistical methods. While some of these criticisms are well-founded, frequently the use of various parametric methods such as analysis of variance, regression, correlation are faulted because: (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. In this paper, I dissect these arguments, and show that many studies, dating back to the 1930s consistently show that parametric statistics are robust with respect to violations of these assumptions. Hence, challenges like those above are unfounded, and parametric methods can be utilized without concern for "getting the wrong answer".

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The factors influencing learning, and the reasons for this surprising finding, are discussed.

Non-analytic reasoning is a central component of diagnostic expertise at all levels. Clinical teaching should recognise the centrality of this process, and aim to both enhance the process through the learning of multiple examples and to supplement the process with analytical de-biasing strategies.

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