An experiment is not considered “barren and negative” when it disproves your conjecture: an experiment fails by being inconclusive.

Successful experiments are partly the product of good experimental designs, as described in Chapter 2; there is also an element of luck (or savvy) in choosing a well-behaved problem to study. Furthermore, computational research on algorithms provides unusual opportunities for “tuning” experiments to yield more successful analyses and stronger conclusions. This chapter surveys techniques for building better experiments along these lines.

We start with a discussion of what makes a data set good or bad in this context. The remainder of this section surveys strategies for tweaking experimental designs to yield more successful outcomes.

If tweaks are not sufficient, stronger measures can be taken; Section 6.1 surveys variance reduction techniques, which modify test programs to generate better data, and Section 6.2 describes simulation shortcuts, which produce more data per unit of computation time.

The key idea is to exploit the fact, pointed out in Section 5.1, that the application program that implements an algorithm for practical use is distinct from the test program that describes algorithm performance. The test program need not resemble the application program at all; it is only required to reproduce faithfully the algorithm properties of interest.