Page 70 - Special Topic Session (STS) - Volume 3

STS515 Steve MacFeely
remain an essential ingredient for any statistician in the future, perhaps the
necessary skills mix will change. For the moment (and I think for the foreseeable
future), three essential skills will be required: numerical skills; statistical skills; and
increasingly, technological skills (see Figure 2). Irrespective of whether we are
discussing a data scientist, an official statistician or a professional statistician in
another field, the requirement for these basic skills is universal. Mathematical
and numerical skills is I think self-explanatory, but crucially a statistician should
be able to spot patterns, understand differences between stocks and flows and
be comfortable reading and writing in scientific notation. Statistical skills means
being able to work with real, often messy or incomplete data. Understanding
bias; both the likely sources and what remedial actions can be taken. Statisticians
should understand the subtle but important differences between accuracy and
precision. They should also develop a good understanding of concepts like
uncertainty and risk. A competent statistician should be able to select and use
appropriate statistical techniques and models. Future technological skills are the
area hardest to predict. Technology is changing rapidly, with consequences not
only for the applications we will use, but also the types of data we may have
access to. Here it is very hard for a university to prepare courses for the future
and for statistical offices to say with any certainty what will be required. If current
trends have anything useful to say, then it suggests a greater use to ‘freeware’
and combining packages. It also suggests a commitment to lifelong learning
will be essential.
Statisticians must understand the underlying logic of theory, so that having
acquired skills, they can apply them and put theory into practice in a variety of
real-life situations (all invariably more complex and messy than the scenarios
presented in text books). Other skills, perhaps neglected in the past, but now
universally recognized as important, is the ability to communicate well and to
present statistics in context. In a world awash with data and cluttered, incoherent
babble, the ability to translate data into coherent statistics and understandable
and digestible messages is absolutely essential. Data visualization is an
important element subset of this skill, but perhaps one where too much
emphasis is being placed at the moment. A statistician can only design an
effective visualization if they are clear themselves what the key messages are.

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