Can I at this point try arguing[1] that this is in fact underanalysis and not overanalysis? As in, to do it properly, you'd have to bring in fuzzy logic and Bayesian inference and lots and lots of qualifiers and biology and all of that good stuff. I mean, it's not as if there aren't notable correlations in matters of taste, preference and emotion.
There seem to be two distinct problems that both get lumped together as "overanalysis". One of these is where you have too little data, of too poor quality, to go on. Essentially I'm talking about statistical insignificance and other related things. This is truly and indisputably deserving of "overanalysis". The second is where the thing you're trying to understand is fearsomely complex, and something cobbled together with simple predicate logic and a few dodgy assumptions just won't do, especially if you've got something in your brain, provided by biology, which can do a much better job "on instinct". This is what I semi-jokingly suggested might be called "underanalysis", but "misanalysis" or better still "malanalysis"[2] may be better.
[1] Translation: This may itself be overanalysis. [2] Now try getting such a horrible-sounding word to catch on...
Gosh, I like the classification of overanalysis types (maybe I am a taxonomist for a reason). What about a situation that is fairly simple and suitable for analysis, but instead of accepting their conclusions and acting on them the person runs the analysis again and again with different input factors, until results are skewed and confused?
Wow, this whole thread seems to be made of over-analysis. o_O
Presumably, given all the above, if I say I prefer Windows because I can do all the stuff I want to more easily than Linux, and the only 'fuzzy logic' I know about is the cute fuzzy T-shirt on ThinkGeek, will all the geeks jump on me now?
Er, guys, are you trying to defend geekery or ramp up the stereotypes? I'm so confused.
<mock-offended>Sides? Me? I'm too busy being a geek here to have a side!</mock-offended> More seriously, I sometimes like to see if I can redefine the sides in a discussion in the hope that it might provide a way forward without anyone having to lose face. That, and the fact that I just like analysing things...
If I had to pick a side; well, I'd say there's more to be gained from engaging in excessive analysis and then being sceptical about the results of said analysis, than in being overcautious about how much analysis you do at all. Consider how science is meant to work (Awooga! Gross oversimplification incoming!): you do some analysis, come up with an idea, but you only accept it when you find that the experiments agree with your analysis.
As for your second sentence; well, you've got the core of a good point there, but I think you might need a bit more work on how you present it.
(Potted summary of fuzzy logic, BTW:
Long, skippable version: you know how you can do all of that boolean stuff where 1 is true and 0 is false? Fuzzy logic extends that so you can have 0.6 or 0.001. Some mathematicians have been able to prove that some sorts of fuzzy logic have all sorts of desirable properties in common with normal logic, but the jury's still out on whether it's possible to do anything useful with it that you couldn't do better with normal logic and some probability, and it's entirely unclear what relation (if any) it has to what the brain does or what sentences mean.
Short version: it's one of those things for dealing with uncertainty and in particular ambiguity that AI types like to argue about.)
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There seem to be two distinct problems that both get lumped together as "overanalysis". One of these is where you have too little data, of too poor quality, to go on. Essentially I'm talking about statistical insignificance and other related things. This is truly and indisputably deserving of "overanalysis". The second is where the thing you're trying to understand is fearsomely complex, and something cobbled together with simple predicate logic and a few dodgy assumptions just won't do, especially if you've got something in your brain, provided by biology, which can do a much better job "on instinct". This is what I semi-jokingly suggested might be called "underanalysis", but "misanalysis" or better still "malanalysis"[2] may be better.
[1] Translation: This may itself be overanalysis.
[2] Now try getting such a horrible-sounding word to catch on...
Presumably, given all the above, if I say I prefer Windows because I can do all the stuff I want to more easily than Linux, and the only 'fuzzy logic' I know about is the cute fuzzy T-shirt on ThinkGeek, will all the geeks jump on me now?
Er, guys, are you trying to defend geekery or ramp up the stereotypes? I'm so confused.
<mock-offended>Sides? Me? I'm too busy being a geek here to have a side!</mock-offended> More seriously, I sometimes like to see if I can redefine the sides in a discussion in the hope that it might provide a way forward without anyone having to lose face. That, and the fact that I just like analysing things...
If I had to pick a side; well, I'd say there's more to be gained from engaging in excessive analysis and then being sceptical about the results of said analysis, than in being overcautious about how much analysis you do at all. Consider how science is meant to work (Awooga! Gross oversimplification incoming!): you do some analysis, come up with an idea, but you only accept it when you find that the experiments agree with your analysis.
As for your second sentence; well, you've got the core of a good point there, but I think you might need a bit more work on how you present it.
(Potted summary of fuzzy logic, BTW:
Long, skippable version: you know how you can do all of that boolean stuff where 1 is true and 0 is false? Fuzzy logic extends that so you can have 0.6 or 0.001. Some mathematicians have been able to prove that some sorts of fuzzy logic have all sorts of desirable properties in common with normal logic, but the jury's still out on whether it's possible to do anything useful with it that you couldn't do better with normal logic and some probability, and it's entirely unclear what relation (if any) it has to what the brain does or what sentences mean.
Short version: it's one of those things for dealing with uncertainty and in particular ambiguity that AI types like to argue about.)