Tenets of Engineering Skepticism

A couple of my old posts talked about engineering skepticism (here).  I’ve been thinking more about it and came up with the following four tenets of ES.  These would be used first to test a claim, process, or machine before it could be considered useful to the engineering world.

Four tenets of Engineering Skepticism

1. The device, process, or method must be effective. It must be clear that there is a real effect without relying on advanced statistics. If the results are less than 25% better than chance (guessing), it doesn’t pass the ES test and can be dismissed as being useful to the engineering world.

2.  It must be reliable. This means that it should work the way it was intended in real life conditions and with any trained operators. If it relies on the weather, the aura of the user, or how the stars are aligned, then it fails the ES test and can be dismissed.

3.  It must be repeatable. If it is used in the same way in the same environment multiple times it should provide the same or very similar results. This should hold true even when done with different operators. For instance, if five different dowsers go through an area and give five different results, the method does not pass this ES test and can be dismissed.

4.  And it must be teachable. If it depends on some peculiar talent or right of birth then it is of no use to engineering. Knowledge must be able to be recorded and passed on to new generations of engineers.

Failure to pass all of these tenets does not necessarily mean that the device or method is fake or false, but that it is not dependable or of enough rigor to be useful to engineering. Until it meets those tenets, any engineer worth his salt will dismiss these devices or claims.

Post a comment if you think any of this makes sense, or doesn’t.


1 thought on “Tenets of Engineering Skepticism”

  1. I wrote something along the lines here. I agree with most of what you say though I would put the 25% much higher.
    However for my field; energy reduction research, my criteria is more focused towards improvement. Here typically see that if the invention does not show a 10-25% improvement as suggested by models based on lab data, then forget it.


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