Thursday, June 21, 2012

Fundamental Flaws of Science

I consider myself a scientist. I am a believer in the principles of observation, measurement, experimentation, and repeatability.

I do notice three rather substantial flaws in the scientific process as we apply it.

The first flaw, is the lack of a ‘truthiness’ metric. I’ll use gravity as an example. If I were to say “acceleration due to gravity”, you might just remember a number in the range of 32 ft/s2 (or 9.8 m/s2). If so, good on you for remembering this little physics tidbit (geek!).

However, 32 ft/ s2 Does not in fact represent the rate at which an object accelerates as it falls. It is a very good approximation for an object falling close to the earth’s surface. 

Gravitational acceleration in fact varies depending upon the distance between, and the mass of, both objects. So being a significant distance from earth versus being close to the surface, or being on a planet with different mass than earth will yield a much different rate of acceleration.

But even for a given altitude from earth, gravitational force varies depending upon location, which means acceleration varies as well (granted the variance is relatively small, thus the 32 ft/ s2 will yield a sufficiently accurate value, but the point is, when you were told that “acceleration due to gravity is 32.1 ft/ s2” in school what you learned was not precisely true).

In reality science seldom defines truth. Instead it defines a model which is believed to approximate truth based upon current observation and measurement. 

In Mechanical and Electrical engineering, for example,  there are many equations that measure flows of things (Water, air, etc. for mechanical and Civil, and electricity for Electrical). Those equations do a good job of accurately modeling these flows… when they are big. But as things get much smaller (you see this often in modern electronics design, now that more computing sits in that phone you are holding than was available in the room sized computers that first put man on the moon), those equations fail miserably to accurately predict behavior, an entirely different set of equations is called upon when dealing with the miniaturized version. What does that mean? It means those equations do not represent the truth. They are good approximations of what is happening for a limited range of conditions, but they do not in fact model what is actually happening.

The problem is, this is seldom well documented, so people often accept as true, that which is only, mostly true, or generally true, or true, but only under certain circumstances.

And that’s the stuff we know we don’t know, what about the stuff we don’t know we don’t know? 

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