  Empirical research plays a fundamental role in the machine learning domain.
At the heart of impactful empirical research lies the development of clear
research hypotheses, which then shape the design of experiments. The execution
of experiments must be carried out with precision to ensure reliable results,
followed by statistical analysis to interpret these outcomes. This process is
key to either supporting or refuting initial hypotheses. Despite its
importance, there is a high variability in research practices across the
machine learning community and no uniform understanding of quality criteria for
empirical research. To address this gap, we propose a model for the empirical
research process, accompanied by guidelines to uphold the validity of empirical
research. By embracing these recommendations, greater consistency, enhanced
reliability and increased impact can be achieved.
