Science and Society: A Reflexive Approach to Official Statistics
3.1 Profound Knowledge—A System’s Approach to Quality
Science and Society: A Reflexive
the system to behave as a single organism that automatically seeks a steady state. It is this steady state that determines the output of the system rather than the individual elements. Tak- ing a systems approach results in management viewing the organisation in terms of many internal and external interrelated connections and interactions, as opposed to discrete and independent departments or processes governed by various chains of command. When all the connections and interactions are working together to accomplish a shared aim, a business can achieve tremendous results from improving the quality of its products and services, to raising the entire esprit de corps of a company.
Knowledge about variation: the range and causes of variation in quality and use of statistical sampling in measurements. In any business, there are always variations—between people, in output, in service and in product. The out of a system results from two types of variation:
common cause and special cause variations. Common cause variations are the natural result of the system. In a stable system, common cause variation will be predictable within certain limits. Special cause variations represent a unique event that is outside the system; for example, a natural disaster. Distinguishing the difference between variations, as well as understanding its causes and predicting behaviour, is key to management’s ability to properly remove problems or barriers in the system.
Theory of knowledge: the concepts explaining knowledge and the limits of what can be known. How do we know that what we think we know is really so? There is no true value of any characteristic, state or condition that is defined in terms of measurement or observation.
The ‘value’ is in the context for a given operational definition. Understanding that a value must be interpreted via context leads us to question any data that does not provide the operational definition for how the data was created. And this leads to better understanding of whether or not the data is really useful, because without having the operational definition we are likely to draw incorrect conclusions from data.
Knowledge of psychology: concepts of human nature. An organisation has a duty to create a system where people can take pride in what they do. By doing so, the organisation is able to focus on continual customer-focused improvement over the long term. Deming’s view is that employees are key to the long-term success of the organisation. They are not costs to be minimised; they are valuable partners in the continuing success of the organisation.
Deming’s teaching on management was very often summarised in the form of principles that have been incorporated into the standard textbooks and TQM training courses. More practically, he condensed his philosophy in ‘14 Points for Management and Seven Deadly Diseases of Management’, for example “5. Management by use only of visible figures, with little or no consideration of figures that are unknown or unknowable” (TheDemingInstitute2018).
Interestingly, it turns out that Deming’s intention was by no means the construction of a monstrous measurement machinery for controlling and accounting, rather the opposite: “It is wrong to suppose that if you can’t measure it, you can’t manage it –a costly myth” (Deming2000, p. 35).
Dr. Deming did very much believe in the value of using data to help improve the management of the organization. But he also knew that it wasn’t close to enough. There are many things that cannot be measured and still must be managed. And there are many things that cannot be measured and managers must still make decisions about. (Hunter2015)
Neither did the other management guru Peter Drucker ever express “If you can’t measure it, you can’t manage it”. Rather, “Drucker’s take on measurement was quite nuanced”, not measurement myopia (Zak2013).
•A system must be managed
•A system cannot understand itself and needs guidance from outside
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•Knowledge is built on theory
•Informa on is not knowledge
•Ra onal predic on requires theory
•Interpreta on of data ...
is predic on
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•Individuals
•Groups
•Society
•Use of data requires knowledge about different sources of uncertainty
•Dis nc on between enumera ve studies and analy c problems
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• Knowledge
about
varia on Psychology
Apprecia on for a system Theory of
knowledge
Fig. 3.1 Components of the system of profound knowledge. See the full composition of Deming’s approach to Profound Knowledge here: Moen and Norman (2016: 50)
The approach used in this chapter follows Deming’s concept. It is all about pro- found knowledge for official statistics. How the system of official statistics is struc- tured and how it works have already been explained in the previous section. The point here is to understand the possibilities and limits of measurement and knowledge cre- ation, and what Deming calls ‘psychology’ in our case, accordingly, the sociology of the interactions between statistics and society (Fig.3.1).
Following the concepts of Russell Ackoff,2a systemic approach will be applied to firstly analyse official statistics in its individual parts and then to get an overview of the whole with its functions in a synthesis.
With such a broad and multidisciplinary approach, however, one runs the risk of completely losing oneself in the expanses and depths of other disciplines, such as the philosophy of knowledge or of sociology. One must therefore select, and priorities must be set. The selection criterion for the treatment of topics as well as the detail of their representation is their relevance to official statistics. ‘Profound knowledge’
does not mean universal knowledge. However, it is a walk on a fine line and the attempt to achieve the optimum between the goal of completeness and the accuracy of detail, even if these goals are in a certain conflict with each other.
2‘Systems thinking is the fusion of analysis and synthesis, depending on whether our objective is knowledge or understanding’ (Ackoff1994).
The second part of this chapter will focus on the most important epistemological questions behind official statistics. It is necessary to ask with what expectations one can and should approach statistics and the results produced by them. Are the reality and statistics that reflect reality one and the same? If there is a difference between reality and its (statistical) image, how does this image emerge, what can it provide and what can it not provide in terms of ‘truth’? How should one deal in general with terminology such as truth, reality, correct, right and wrong? What is the appropriate manner of a statistician to communicate results without risking committing the fallacy of misplaced concreteness?3
The third will deal with scientific approaches that analyse the ‘co-construction’
phenomenon: “Statistics are often seen as simple, straightforward, and objective descriptions of society. However, what we choose to count, what we choose not to count, who does the counting, and the categories and values we choose to apply when counting, matter”.4This part will concretise the co-construction of statistics and society for selected historical episodes and present the current mainstream way of thinking as far as is relevant for official statistics.
The fourth and fifth parts will apply the lessons learned to a more general discussion of statistical indicators and metrics for Sustainable Development.