Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results

TL;DR

This paper highlights how flexible choices in benchmark study design and analysis can lead to biased, overly optimistic results, emphasizing the need for careful, transparent research practices.

Contribution

It demonstrates the impact of multiple design and analysis options on benchmark results and advocates for awareness and transparency to improve reliability.

Findings

Benchmark results vary significantly with different design choices.

Multidimensional unfolding can assess the impact of analysis options.

Questionable practices can bias interpretations of benchmark studies.

Abstract

In recent years, the need for neutral benchmark studies that focus on the comparison of methods from computational sciences has been increasingly recognised by the scientific community. While general advice on the design and analysis of neutral benchmark studies can be found in recent literature, certain amounts of flexibility always exist. This includes the choice of data sets and performance measures, the handling of missing performance values and the way the performance values are aggregated over the data sets. As a consequence of this flexibility, researchers may be concerned about how their choices affect the results or, in the worst case, may be tempted to engage in questionable research practices (e.g. the selective reporting of results or the post-hoc modification of design or analysis components) to fit their expectations or hopes. To raise awareness for this issue, we use an example benchmark study to illustrate how variable benchmark results can be when all possible combinations of a range of design and analysis options are considered. We then demonstrate how the impact of each choice on the results can be assessed using multidimensional unfolding. In conclusion, based on previous literature and on our illustrative example, we claim that the multiplicity of design and analysis options combined with questionable research practices lead to biased interpretations of benchmark results and to over-optimistic conclusions. This issue should be considered by computational researchers when designing and analysing their benchmark studies and by the scientific community in general in an effort towards more reliable benchmark results.