Experimentation and Results Representation

Once experiments are designed, the next step involves execution.
Key tasks include:
- Running test cases multiple times.
- Using the specific ranges of the experimental factors during testing.

After executing the experiments, it is crucial to compute metrics based on the test results.
Steps include:
- Calculating averages over the end times from the various test cases run.

The representation of results is a critical component.
Note that a detailed discussion on best practices for result representation will not be provided.
It is emphasized that visual representation of results strengthens arguments.
- Effective visualization can enhance clarity and impact of findings.
Suggested resources for guidance include:
- Various papers discussed in the course that show different techniques of result representation.
- Online documentation and materials.
- Relevant courses offered at Georgia Tech and Udacity focusing on information visualization.

It is not sufficient to merely present the results.
Conclude by explaining what the experimental results support in relation to the claims made.
Clear articulation of conclusions drawn from data is essential for effective communication of findings.