Let us be honest with ourselves: Most biostatistics courses teach biologists how to perform statistical analysis, but virtually nothing about what can be inferred from http://www.binaryforexacademy.com/the-millionaires-maker-review/ their results, what those inferences are assuming, nor what is possible if those assumptions are incorrect. At least, if biologists are aware of those issues, judging from what is published in scientific journals, they are generally ignored. This situation has been described repeatedly over several decades, but remains largely unchanged, even among premier journals. Since improvements in some areas of medical research were attributed to strongly stereotyped designs and analyses which now prevail, a few statisticians consider the way forward is to further constrain biologists freedom of choice in this respect. The cure for inept erroneous thinking is not to outlaw judgement and innovation, but to improve the quality of understanding. That said, the attempt to only use stereotyped designs and analyses seems doomed to failure, partly because of the sheer variety of biological research (most noticeably in ecology) and partly because few statisticians (other than epidemiologists) are happy to propose pre-cooked mechanical procedures for observational (rather than experimental) studies. Statistical editors have attempted to impose some order upon matters, but the results are at-best uneven. Medical journals for instance, strongly prefer confidence intervals over tests, and (apparently) prefer treatments and outcome variables to be collapsed to binary scales.

Ecologists employ virtually every sort of design and analysis, and describe them very differently from clinicians, but exhibit no more awareness of their reasoning or assumptions. Veterinary research remains largely unchanged, and continues to promote premature alopecia among statisticians. Given the clamour to improve statistical refereeing has proven rather ineffectual, perhaps the answer is to improve the quality of what is being refereed? Whilst a statistically valid survey of biostatistics courses for biologists is beyond our means, as far as can be judged the following analysis would not be grossly in error: At the undergraduate level, most biologists are still provided a 'watered-down' first-year undergraduate statistics course - on the basis that their needs are the less. Unfortunately, biologists do not require less statistical training, but a different training. The assumption that biologists can consult statisticians for matters beyond their understanding seldom works when neither party understands the other's underlying reasoning and they do not share a common terminology. A solution to these problems is not to dumb-down statistics courses, but to approach this subject in another way. For instance why not broaden the scope of biostatistics lectures, from providing tightly focused sets of 'recipes', to critically examine and compare real problems and to compare differing approaches to those problems? A rather different problem arises because different biological disciplines employ very different conventions, terminology, and popular designs/analyses. One result thereof is biological research is progressively compartmentalized.
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