Data exploration in phylogenetic inference: scientific, heuristic, or neither.

Area of Science:

• Phylogenetic inference and evolutionary biology.
• Systematics and data analysis methodologies.

Background:

• Data exploration methods are central to phylogenetic inference but their scientific importance is often unexamined.
• Existing methods are frequently employed without rigorous evaluation of their empirical testability or heuristic potential.

Purpose of the Study:

• To critically assess the scientific and heuristic value of various data exploration methods used in phylogenetics.
• To determine if these methods provide empirical tests or merely heuristic insights for phylogenetic analysis.

Main Methods:

• Review and analysis of over 20 data exploration techniques, including sensitivity analysis, saturation analysis, and quality assessment methods.
• Evaluation of methods based on their ability to perform empirical tests that could potentially refute hypotheses.
• Distinction between scientific methods (empirical refutation) and heuristic methods (pointing to weakly corroborated hypotheses).

Main Results:

• The vast majority of data exploration methods reviewed are neither scientific nor demonstrably heuristic.
• Only character compatibility analysis (and to a lesser extent, character congruence) can be considered scientific, though weak.
• Methods like Bremer support and long-branch extraction are heuristic, but their potential is often underestimated.

Conclusions:

• Undue emphasis is placed on data exploration methods in phylogenetic inference due to their limited demonstrated cognitive worth.
• Phylogeneticists are urged to more carefully consider the relevance and scientific validity of the methods they employ.
• Prioritizing clarity, simplicity, and truth over technical complexity is crucial for advancing phylogenetic research.