How-to-Write-a-Better-Thesis

Presentation
101
Presentation
In the previous chapters of your thesis you described the design of your work, explaining
how it tested your hypotheses or answered your research questions. You
now have to present the results you obtained in this work.
This presentation should not be haphazard. The presentation should educate the
reader. You may believe that your task is to include every single data point or case
that you recorded in your work—but doing so is almost certainly a mistake. You
have used this data to draw conclusions as objectively as you can; now the task is to
use representative examples drawn from the data, and example analyses of the data,
to persuade the reader of the validity of these conclusions.
In any case, even when the data is limited it is surprisingly difficult to capture
it all within the confines of a thesis. In Don’s case, even a brief explanation of a
single ‘meal episode’ might take a page or two; in Dai’s case, a single transcript of
how his method was used in the course of a study of a chemical structure might take
ten or more pages. Jorge’s raw results had millions of individual data points, and
thousands of secondary products could be built on these, such as tables and graphs
showing the cost of his simulation method under different assumptions. Inclusion
of all the data is unlikely to be feasible.
And what would be the point of simply dumping the data into the thesis? It is unlikely
to be meaningful to the reader. Here are the things the reader needs to know,
some of which may have been covered in earlier chapters:
• How the data was gathered—where it was sourced from, what aspects of it were
measured, what it consists of, what the guidelines were, what permissions were
required, what restrictions apply, and so on.
• How the data might be obtained by a reader—whether directly from you, or from
what external source; or how similar data might be created.
• What the results looks like—by example; or by graph, to show, say, the distribution
of values; or by tables of typical instances. For example, a common strategy
is to list out the categories into which the data can be placed, and give an example
of an item in each category.
• Summaries of the complete set of results, in as rich a way as possible.
• Notes of issues such as known gaps or incompleteness in the results, or where the
data may be uncertain or unreliable.
• Analyses of the results, using discussion, argument, statistical tools, and so on,
as appropriate to the work.
• Interpretation of the analyses, completing a transformation from data to knowledge
(more on this later).
Inclusion of the raw data is not in this list. Consider Tony’s work on laser grading
mentioned in Chap. 6. This included focused interviews, lasting about one hour
each, with three farmers and several professionals in the area of land management.
All were recorded and transcripts of them prepared. Should Tony have spent so
much time preparing transcripts? Having prepared them, should he have included