The etymology of “statistics” points to the Latin verb stare (= to stand) as the far origin of the word. In addition to the meaning to stand, stare gave a handful of derivative words. More specifically, it gave the words “status”, or the “state” of something or someone, and the word “State”, which designates the government. In late Latin, the adjective statisticum was present in the administrative discourse to refer to something “that concerns the State”. This latter word gave today’s word “statistics” (Cellard, 1980). Statistics now refer intuitively to a quantitative (and positivist) approach to our scientific quest for knowledge, in opposition to a qualitative (and interpretive) approach.
In modern times, statistics became widely used for developing our understanding of the universe in which we live, but also for monitoring health and other social issues that affect human populations (cf. Daskalakis, p. 28). Thanks to the many disciplines built using statistical analysis and mathematics, countless industries, organizations and institutions resort nowadays to data collection and analysis to determine their agenda, manage and evaluate their activities and advance their objectives. It seems, therefore, inevitable for the modern man to develop a keen sense of what statistics are and the basics of its usage or “statistical literacy”.
Far from saying that a quantitative approach is the only sound means of scientific investigation, qualitative studies and social science’s inductive approach, such as the grounded theory, have enabled researchers to unveil much of why and how social dynamics take place. Much of this work contributed to the development of the determinants of health that we are using today in public health policymaking.
Nevertheless, statistics remain at the heart of most biomedical and science research as they convey a sense of objectivity in our observation of the world. Statistics are considered “neutral” in the sense that their collection and analysis should not depend on the observer’s perception: anyone with the same measuring instruments and in the same environmental conditions should find equal results – the experiment is reproducible. With appropriate control variables, analysts are able to reduce interpretation biases that may impede objectivity and, thus, the validity of the experiment (cf. de Broucker, p. 20). Yet, the objectivity of statistical analysis is always debatable (cf. Evans, p. 10), and the validity of an empirical investigation highly relies on the skills of the analyst.
The present issue features commentaries from key figures of the statistics world as well as articles and essays from students who are enthusiastic to apply what they learned in statistical analysis and research methods courses.
I would like to thank warmly our guest contributors Constantine Daskalakis, Patrice de Broucker, Michael Evans and John Pullinger for their support to this student initiative to engage students in developing and sharing their own research work in health sciences. I would also like to thank Katrine Dragan, the designer of this issue’s trendy cover page; Raywat Deonandan, our academic counsellor; all the students who met the challenge of presenting contributions to the peer-review process and the editorial team who, through their hard work, contributed directly to the development of students and the advancement of knowledge in health sciences.
Cellard, J. (1980). Les 500 racines grecques et latines les plus importantes du vocabulaire francais (Vol.2). Paris-Gembloux (France): Editions Duculot.
Haverland, M., & Yanow, D. (2012). A Hitchhiker’s Guide to the Public Administration Research Universe: Surviving Conversations on Methodologies and Methods. Public Administration Review, 72(3), 401-408.
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