Statistics in dental research: A challenge for a dental materials scientist

Dental research relies heavily on statistics and in the majority of studies some sort of statistics is necessary. This goes beyond the descriptive statistics (the measures of central tendency and spread) and includes hypothesis testing using parametric or non-parametric tests. Sometimes other tests are used depending on the research question and the hypothesis. As far as I can remember, the only type of research where I haven't seen any statistics done in dental materials science is finite element analysis which involves computer simulation of stresses and strains on bone and/or tooth models. This approach does not require sampling and therefore no statistics is performed.

The validity of results and conclusions depends, among other things, on the appropriate statistical test(s). I'm pretty sure dentists and material scientists who conduct research but are not familiar with statistics feel this may be their main weekness. In all research methodology courses, it is strongly advised to consult a statistician prior to conducting a study because even in the planning stage of the study, statistics is unavoidable as it is necessary to perform sample size and power calculation. However, consulting a statistician is easier said than done simply because there are not very many statistians out there available for quick (and free of charge) consultations. It seems to be a matter of personal initiative to establish some contacts since many academic institutions don't have statisticians among their employees.

Having said that, I can't help asking myself the following when I read scientific papers: how did these authors perform statistical analysis? Did they consult a statistician? Did they do statistics themselves? What's their knowledge on this subject and did they test the hypothesis based on the correct assumptions? Did they just copy the same test from a similar paper published previously? These questions arise because in many papers only the applied test and the p value are stated. Very little or nothing is known about the assumptions for parametric testing, how the departure of the required assumptions were dealt with, possible outliers and their effect on the results, correction in multiple testing etc.

I would appreciate some input from fellow scientists so please feel free to comment on this and write your opinion. Your own or other people's experience is welcome.

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