Philosophers Challenge the Boundary Between Data and Phenomena in Science

Scientific data and natural phenomena might not be as fundamentally separate as philosophers have long assumed, according to a critical analysis of a decades-old scientific distinction. The boundary between raw experimental records and real features of the natural world breaks down under close scrutiny, suggesting that both concepts actually operate as parts of a single, continuous feedback loop.

Decoding the Bogen and Woodward Distinction

The philosophical framework introduced in 1988 by philosophers James Bogen and Carl Woodward claimed that scientific theories explain phenomena rather than data. Phenomena were defined as stable, real-world features inferred from measurements, while data were characterized as public records produced by instruments in specific experimental settings. For nearly four decades, this distinction shaped how philosophers of science view laboratory practice, aiming to prevent circular reasoning where a theory simply explains the very data it generated.

However, Samuele Fasol from Foundations of Science argues that this foundational claim suffers from persistent ambiguity. The assertion that theories do not explain data can be interpreted in three distinct ways, none of which fully withstands critical evaluation. These competing interpretations have fueled years of debate among science scholars without reaching a definitive consensus.

To untangle the confusion, the analysis published in Foundations of Science systematically dismantles each reading of the original argument. The first interpretation suggests data can never be systematically explained by theories. Critics point out that this standard applies equally to phenomena, which also involve idealized abstractions rather than capturing every messy, multifactorial detail of reality.

Can Theories Explain Experimental Records?

The second interpretation proposes that while data can sometimes be explained by theories, such explanations remain scientifically uninteresting or relegated to the early stages of a discipline. Counterexamples from the history of physics and chemistry challenge this assumption. Researchers routinely utilize theoretical models to understand how instruments produce specific signals, demonstrating that explanations of data play an active and essential role in mature scientific inquiry.

A third, largely overlooked interpretation suggests that data can indeed be systematically explained, but only by different theories than those explaining the corresponding phenomena. For example, optics might explain how a telescope captures starlight, while astronomy explains the planetary phases inferred from those observations. Yet, this approach collapses the strict boundary between the two categories, turning what counts as data in one theoretical framework into a phenomenon in another.

A Circular View of Scientific Progress

Rather than treating data and phenomena as fundamentally distinct ontological categories, the study points toward a circular model of scientific knowledge. In this interconnected view, theoretical frameworks guide the detection of empirical data, and those same data feed back into the evaluation and revision of the overarching network of theories.

This cyclic process avoids vicious circularity because empirical observations retain the capacity to surprise researchers and undermine faulty theoretical assumptions. Ultimately, the conceptual side of science shapes how empirical data emerge, while empirical discoveries instruct our conceptual models how to adapt and change.

The research was published in Foundations of Science on November 6, 2026.