Page 69 - Special Topic Session (STS) - Volume 3

STS515 Steve MacFeely
statistically literate in a reasonably short space of time. Statistical literacy can
mean different things to different people, so for the purposes of this paper, a
very broad definition or concept of statistical literacy is used (see Figure 1). There
are many definitions of statistical literacy [e.g. 1]. For the purposes of this paper,
I explain statistical literacy as the nexus or intersection point between traditional
literacy (which includes reading, writing and mathematics, but also a wider
appreciation of history, geography, legal issues, ethics and culture); technological
literacy (which includes knowledge of databases, software and increasingly,
social media); and data literacy (which requires an understanding of primary and
secondary sources, including big data and GIS, open data, and an understanding
of the Generic Statistical Business Process Model). Statistical literacy straddles all
of these, requiring elements from each of these dimensions, but in addition,
requiring an appreciation of statistical techniques and modelling. Put together,
a statistician should understand the broader context of what is being measured
1
– including the history and the appropriateness of the measurement tool .
Figure 1. The Statistical Literacy Nexus

2. Required Skills
Thinking about NSOs and NSSs of the future, it is very hard to anticipate
what specific skills will be required. That said, at this juncture, it is hard to
imagine that the ‘hard’ skills expected of a statistician today not remaining
relevant. Arguably, these core statistical skills will become more important,
rather than less, as graduates become more accustomed to using blackbox
software packages but perhaps less accustomed to thinking about the
underlying concepts and methodologies. While hard skills will most likely

1 This view of literacy overlaps considerably I think with the views expressed in the ProCivicStat
(2018) - http://community.dur.ac.uk/procivic.stat/
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