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Statistics in physiology and pharmacology: A slow and erratic learning curve.


Ludbrook, J.

Clinical and Experimental Pharmacology and Physiology, 28(5-6), 488-492 (2001).

1. Learning how to apply statistical analyses to the results of experimental or clinical studies may take a lifetime of trial (and sometimes error), as it has done in the author's case. There is no evidence that biomedical investigators of the present generation are on a steeper learning curve. Gross misunderstandings of the purpose and functions of statistical analysis are apparent in applications to research grant-giving bodies and ethics committees, in manuscripts submitted to journals and sometimes in published papers. 2. Although estimation of minimal group (sample) size for a given power is an essential step in planning clinical studies, it seems to be used rarely in laboratory experimental work. This is despite exhortations to restrict the number of animals used to a minimum. 3. Most investigators use hypothesis testing to analyse their results, but their understanding of the meaning of the resultant P-values is slight. 4. A flaw found almost universally in biomedical manuscripts is to make multiple inferences from the results of a single study. The goal of statistical analysis is to maintain the familywise type I error rate (risk of false-positive inference) at a predetermined level (usually 5%). But, when multiple inferences are made from the same experiment, the risk of false-positive error is inflated. There are two solutions to this problem: (i) use a multiple comparison procedure to control the familywise type I error rate; and (ii) test a single, global hypothesis. 5. Biomedical investigators have been quick to acquire computer statistics software and to use it to analyse their experiments. However, they have been slow to recognize the limitations of this software. These include: (i) inadequate documentation of routines, so that neither the user nor the reader of published papers can be sure how the tests have been executed; (ii) flawed algorithms for the execution of statistical procedures; and (iii) failure to recognize that the best software for their purposes is that which takes them just beyond their statistical horizons. 6. The obvious solution to these difficulties is to recruit a biomedical statistician into every research group, at a relatively trivial cost. However, properly qualified biostatisticians are in desperately short supply in Australia. It follows that research groups, national grant-giving agencies and academic institutions must make provision for the proper training and subsequent employment of biostatisticians.