9 - Interpretation
Published online by Cambridge University Press: 07 September 2011
Summary
Interpretation is here concerned with relating as deeply as is feasible the conclusions of a statistical analysis to the underlying subjectmatter. Often this concerns attempts to establish causality, discussion of which is a main focus of the chapter. A more specialized aspect involves the role of statistical interaction in this setting.
Introduction
We may draw a broad distinction between two different roles of scientific investigation. One is to describe an aspect of the physical, biological, social or other world as accurately as possible within some given frame of reference. The other is to understand phenomena, typically by relating conclusions at one level of detail to processes at some deeper level.
In line with that distinction, we have made an important, if rather vague, distinction in earlier chapters between analysis and interpretation. In the latter, the subject-matter meaning and consequences of the data are emphasized, and it is obvious that specific subject-matter considerations must figure strongly and that in some contexts the process at work is intrinsically more speculative. Here we discuss some general issues.
Specific topics involve the following interrelated points.
To what extent can we understand why the data are as they are rather than just describe patterns of variability?
How generally applicable are such conclusions from a study?
Given that statistical conclusions are intrinsically about aggregates, to what extent are the conclusions applicable in specific instances?
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- Principles of Applied Statistics , pp. 159 - 183Publisher: Cambridge University PressPrint publication year: 2011