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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.

0 replies on “Scientific Results As Data Interpretations”

Spot on as usual. This is exactly at the heart of the matter as I’ve said elsewhere here and in other places. But it has ever been thus and doesn’t detract from science’s usefulness.

However, interpretation always runs the risk of being subject to bias (“underlying assumptions and worldview”) and this is how Francis Bacon put it in Novum Organum (1620):

There are four kinds of illusions that block men’s minds. For instruction’s sake, we have given them the following names: the first are called idols of the tribe; the second idols of the cave; the third idols of the marketplace; the fourth idols of the theatre.

He goes on to explain all four. Essentially, tribe is human nature, cave is the individual, marketplace is from association with others and theatre is from the “illusions which have made their homes in men’s minds from the various dogmas of different philosophies, and even from mistaken rules of demonstration.”

Now I’m not saying that nothing’s changed since then. But in the long run (sounding like an economist) the science will out since the interpretation will lead to predictions/forecasts/scenarios that can be tested empirically. Thisn is why it’s so dangerous for the Royal Society (and also the BBC) to declate their stances and stick to them.

Some social sciences acknowledge this interpretation quite clearly – economics for example talks about positive and normative economics. But this whole thing is a mine-field with fact/opinion, data/interpretation, is/ought, objective/subjective being at the very heart of epistemology.

It’s another reason why the semantics of climate change are so critical to perpetuating the myths as many/most/some words bring with them their own baggage, witness “pre-industrial”, “unprecedented” etc etc.

Scientists do need to champion their hypotheses because if they don’t, who will? Which often involves creative interpretation of data, ad hoc justifications for why an expected outcome did not succeed, etc. No one should have a problem with this approach, but the general public and the media are generally ignorant of the messy nature of scientific activity.

Very good point.

If you can not PREDICT based on your theory and data, there is a lack.

We see the same issues with Cosmological Big Bang theory as AGW. They are poor at predictions.

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