Propeller Head Plaza

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RE: Searching for truth?

Posted by rick_m on November 10, 2014 at 21:49:18:
"I don't necessarily agree with the author that if you don't know how to improve the detectability (SNR) then the hypothesis probably not scientific."

I suspect it may just be a rule of thumb. Perhaps he's saying that the odds aren't very good from his experience.

"As a counter-example, where would particle physics be if we had rejected the standard model back in the 1960s on the basis of warning sign #3 - that many of its predictions were essentially unverifiable with known technology and involved nearly undetectable weakly interacting particles? Or if we had rejected it on the basis of warning sign #7? Instead, we invested heroic amounts of money and effort trying to confirm the theory's predictions, and it turned out to be right."

I think that is actually an example rather than a counter-example. Would folks spend the tons of money to build the LHC if they were totally confident in the outcome? Cern and it's ilk are all about improving the S/N enough to see if the theories held water.

Please bear in mind that I am not especially a fan of the author, to the best of my ability I am neither a "believer" nor a "skeptic". I just thought his comments re altering the S/N were interesting.

"I agree that a good first step to tackling a problem is "observing the problem while futzing with as many variables as possible to ferret out those that affect it." That's how I usually start troubleshooting. However, I don't think it's nearly as effective at optimizing."

I think you're right. You might manage to get it within Spec. or "good enough" but unless you're incredibly lucky it won't be optimum. It especially won't be optimum over component variations.

"When troubleshooting, you are starting from having observed a problem and it's usually a pretty easy observation to repeat and confirm. But when you experiment to find improvements, I find the observations to be a lot noisier and the variables are not always independent."

True, too true...

"And then you have to deal with patternicity - human nature seeking explanations for patterns that are essentially random, or at least caused by some uncontrolled variable that we weren't interested in. This seems to be a common problem in machine learning and other fields involving statistical classification, where the classifier adapts to features evident in random data which are not caused by the underlying function/mechanism of interest. There is a similar tendency in human perception, which evolved to evade predators and hunt prey. Our minds are well adapted to identify patterns in sensory data and then learn to recognize them. It seems like we're hard wired to to over-classify; we tend to see and interpret patterns even in random data, and don't easily recognize randomness unless we're looking at a sufficiently large data set where the distribution is obvious."

Makes sense to me that "It seems like we're hard wired to to over-classify". Under-classifiers probably made a succulent meal before reaching breeding age!

"I think this is part of the reason why there's a lot of tail chasing in audiophiledom. When you approach the optimum then it becomes harder to control variables and harder to separate an effect from noise, and you can get off-track chasing meaningless patterns in noise."

Yea... the S/N of the ERROR get's too small. That's why I suggested that:

'As audiophiles we want to optimize the various factors that go into our listening experience, but the battle ironically requires first improving the S/N of our ability to sense the problems to where we can understand and control them. Then maybe we can improve the S/N of the desired information...'

To reduce problems you need to understand them.

At least it all makes for an interesting hobby...

Regards, Rick