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statistics for non-statisticians

Stats for the million

Felix Grant shares the great statistical work being done by non-statisticians

volunteers, while minimising the impact of inevitable errors. Off duty, my inbox is rarely without a request for informal guidance or reassurance on a piece of data analysis in which the sender lacks confidence. Statistics is a vital, all-pervasive tool,


comparable to driving a car and oſten with as little detailed knowledge of what goes on under the bonnet. In the real world, whether we statisticians like it or not, an overwhelming majority of statistically based decisions and statistically oriented actions are taken by non- statisticians. Having made such a sweeping statement,

it would be prudent to take a moment for definition of terms. What, exactly, is a non- statistician? Tere is no single, clear-cut answer to

that, which is why I used the alternative description ‘lay staff’ in my first paragraph, above. Individuals with the same level of expertise will place themselves on opposite sides of the line. In one market segment with which I am very familiar, professionals who are hazy about the difference between mean and mode are counted as statisticians and make million-euro decisions. In another, l

significant proportion of my consultancy work boils down to maximising the validity and quality of statistical work by lay staff or

science graduates whose transcripts include all the usual statistics courses claim to be completely baffled by the subject. In the 24 hours before writing this, for



example, I have advised a mathematical physicist that using the arithmetic mean of percentage data is not a good plan, a dietician investigating health impacts of recession, a literary theorist wrestling with contingency factors in a comparative semiotic analysis, and a group of conservation volunteers looking for patterns in a sprawling set of ad hoc arboreal data. None of these people, advanced in their own fields, are statisticians. In any professional

literature, particularly in the medical and

life sciences, there is frequent reference to this. Discussing the widespread use of polytomous logistic regression models in cancer case control studies[1]

earlier this

year, for example, Xue and others point out that ‘the validity, accuracy, and efficiency of this approach for prospective cohort studies have not been formally evaluated’, and later in the same paper refer to ‘SAS and S-plus programming codes... provided to facilitate use by non-statisticians’. None of this should be seen only as a

problem. Any healthy organisation seeks to draw upon all the strengths of all its human components, not just those defined by particular labels. One of the most inspiring examples I have personally encountered, at


the opposite end of the spectrum from my examples so far, is a medical research worker whom I will refer to here as Carla. Having failed to graduate from high

school, Carla started work as a beautician’s apprentice in a mortuary. Reasonable typing speed shiſted her into a temporary clerical job within a medical school, and hard work made the job permanent. But her progressive promotion, despite lack of any formal training or qualification, to research analyst was driven by a talent for intuitively interpreting experimental data passing across her desk. Carla doesn’t make any final research decisions, but from her instinctive feel for data (and with the support of good soſtware) she provides an extraordinarily high proportion of successful initial leads for those who do. Carla is a deliberately extreme example, of

course, but far from unique – and illustrates a universal principle.

Exploration of gynomorph triplet data in Minitab JUNE/JULY 2013 17


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