Information Technology Reference
In-Depth Information
robust research outcomes it is essential to have a hypothesis. This chapter concerns
hypotheses and research questions, and how we use evidence to confirm or dis-
prove them.
Hypotheses
In outline, an example research program might proceed as follows.
A researcher investigating algorithms might speculate as to whether it is possible
to make better use of the cache on a CPU to reduce computational costs.
Preliminary investigation might lead to the
hypothesis
that a tree-based structure
with poor memory locality will be slower in practice than an array-based structure
with high locality, despite the additional computational cost.
The hypothesis suggests the
research question
of whether a particular sorting
algorithm can be improved by replacing the tree structure with the array structure.
The
phenomenon
that should be observed if the hypothesis is correct is a trend: for
example, as the number of items to be sorted is increased, the tree-based method
should increasingly show a high rate of cache misses compared to the array-based
method.
The
evidence
is the number of cache misses for several sets of items to be sorted.
Alternatively, external evidence might be used, such as changes in execution time
as the volume of data changes.
As this example illustrates, the structure of the research program flows from having
a definite research question and hypothesis.
A hypothesis or research question should be specific and precise, and should be
unambiguous; the more loosely a concept is defined, the more easily it will sat-
isfy many needs simultaneously, even when these needs are contradictory. And it is
important to state what is
not
being proposed—what the limits on the conclusions
will be. Consider an example. Suppose P-lists are a well-known data structure used
for a range of applications, in particular as an in-memory search structure that is fast
and compact. A scientist has developed a new data structure called the Q-list. Formal
analysis has shown the two structures to have the same asymptotic complexity in
both space and time, but the scientist intuitively believes the Q-list to be superior in
practice and has decided to demonstrate this by experiment.
This motivation by belief, or instinct, is a crucial element of the process of science:
since ideas cannot be known to be correct when they are first conceived, it is intuition
or plausibility that suggests themas worthy of consideration. That is, the investigation
may well have been undertaken for subjective reasons; but the final report on the
research—that is, the published paper—must be objective.
Continuing the example above, the hypothesis might be encapsulated as
Q-lists are superior to P-lists.
But this statement is not sufficient as the basis of an experiment: success would have
to apply in all applications, in all conditions, for all time. Formal analysis might be
