Algorithms Research & Evidence-Based Practice 2

Question 1

In algorithm research, what does 'empirical evaluation' refer to?

Accepted Answer

Testing an algorithm on real or synthetic datasets to measure performance

Answer

Empirical evaluation involves running an algorithm on actual inputs to observe practical performance, complementing theoretical analysis.

Question 2

Which experimental design principle helps isolate the effect of a single algorithmic parameter?

Accepted Answer

Ceteris paribus (all else equal)

Answer

Ceteris paribus—varying one parameter while holding others constant—isolates the causal effect of that parameter on performance.

Question 3

Why do algorithm researchers use multiple independent random seeds when benchmarking randomized algorithms?

Accepted Answer

To reduce bias from a single lucky or unlucky run

Answer

Multiple seeds average out variance inherent in randomized algorithms, giving a more reliable estimate of expected performance.

Question 4

What is the purpose of a 'baseline algorithm' in an algorithm research paper?

Accepted Answer

To serve as a reference point that highlights the proposed algorithm's improvements

Answer

A baseline is an established method against which the new algorithm is compared to demonstrate relative improvements.

Question 5

Which measure best captures the variability of runtime results across repeated trials?

Accepted Answer

Standard deviation

Answer

Standard deviation quantifies how spread out individual trial runtimes are around the mean, capturing variability.

Algorithms Practice Test