104 8 Outcomes and Results
variable against every other possible variable in an effort to analyze the data. His
readers were given so much information, at such a low level, that they were totally
overwhelmed, and learnt nothing about the system being investigated. The candi-
date should have confined himself to plotting charts that tested his hypotheses or
that demonstrated something significant.
My dissatisfaction with Geoff’s chapter on results was due to its unfinished na-
ture. Hypotheses had been tested, but we did not know what had happened. An aim
had been stated at the beginning of the chapter (to report the results of experiments
to test hypotheses), but it had been only partially fulfilled.
If you turn back to Chap. 4, in which the structure of chapters is discussed, you
can see how important it is that you state the purpose of the chapter in the introduc-
tion, and that you write a conclusion in which you describe how that purpose has
been fulfilled. This rule is as important for the results chapter as for any other. At the
end of the chapter, you should share with your readers your understanding of what
is now known that was not known when the chapter began.
Reasoning From Data
A common problem for each of Jorge, Dai, Jackie, and Don was that they had formed
views based on their data but had not yet done the detailed work of building an ex-
plicit argument that presented what it was in the data that supported these views.
The aim is to use your data to make a case for the proposition being explored in your
thesis. Consider the dictum: ‘data is not information, information is not knowledge,
and knowledge is not wisdom’. As soon as you use data to test a hypothesis—that is,
make a link between a proposition and some observations—they become informa-
tion (what the data tells us). Temperature measurements collected by the weather
bureau becomes information when, say, used in conjunction with records of plant
growth to test the hypothesis that plants grow faster at higher temperatures. Simi-
larly, the data collected using your research instruments becomes information when
you use it to test the hypotheses that led to design of the instruments.
Information becomes knowledge when you use an argument to draw conclusions
from it: that plants do grow more vigorously at higher temperatures, for example.
Here, the argument—a chain of reasoning—is an explanation of how the informa-
tion demonstrates the conclusions, and will probably need to explain why other
plausible interpretations of the information are likely to be incorrect (that is, you
will need to eliminate alternative explanations). The argument will also need to
explore the subtleties of the data—to demonstrate that the number of measurements
is sufficient for statistical significance, for example.
Knowledge becomes wisdom when it is integrated into your whole way of look-
ing at things. It is the implications of the conclusions you draw from your results
that become wisdom: new insights, new theory, new frameworks. That is, your
results chapter is a keystone of the hypothesis-evidence-argument-theory (HEAT)
structure of much research, which can be sketched as: