Scientific Results As Data Interpretations

A must-read for anybody involved in disputes about “what Science says”, from today’s e-mail newsletter by The Scientist:

The Problem of Perception – by Steven Wiley

There is a common perception among young students that the surest path to resolving scientific controversies is to design a clever experiment, one that will definitively resolve conflicting hypotheses. However, I have found that most scientific controversies do not revolve around specific experimental data, but instead are disputes over data interpretation. Data interpretations depend on a scientist’s underlying assumptions and worldview. […]

we were working from a computational model of endocytosis that allowed us to try out different sets of assumptions and see how they would affect the system’s behavior. The other group felt that our computer model was a poor substitute for their own scientific intuition regarding what was happening. […]

Interestingly, our view was vindicated not because people came to accept our use of computational modeling, but because our hypothesis was more successful in predicting subsequent experimental results. Scientists don’t generally care about who is right or who is wrong in a dispute. They want a conclusion that can help predict their own experimental outcomes. Science is built brick by brick from ideas and concepts that can lead to the next successful series of experiments and concepts. If an idea doesn’t support the next brick, it is discarded. It’s natural selection in science.

Scientific disputes seem inevitable in any career, but mine gave me a keen appreciation of the need for caution in accepting simple interpretations of the behavior of complex systems. In science, we do not gather facts. We make observations. Our interpretation of observations is only as good as our assumptions and conceptual frameworks. […]

The above explains how AGW could become such a consensual paradigm for an intellighentsia that has lost all hopes. It is also relevant to the discussion about the use of computer models and the extreme importance of their predicting powers. And finally it states loud and clear how pointless it is to pretend that there is nothing subjective in Science, and especially in the study of complex systems.