## Negative consequences on knowing data transitionally

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### Negative consequences on knowing data transitionally

If we are to know any of the subject, what negative consequences would occur on knowing it transitionally?

Here transitional knowing = knowing data non historically or knowing data not in the way it has occurred.

[I have typed below all the consequences, I know, but need to know on the existence of any other consequence, before knowing any of the data transitionally]

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What occurred in the past, might determine the conformations which occur later, in certain conformations. For example, if we see the origin of universe, and if we are knowing any of the data which has occurred after it, it seems that the conformations/structures (as elements), which seem to have occurred at the beginning seem to determine the conformations/structures coming after it. Transitional knowing might not allow knowing elementary working/principles.

If we know any of the data, non historically, we may not be able to know the data, as it really is. Transitional knowing or non historical knowing, might be of utility, if it is directly allowing to attain the desired utility (for example, using a laptop might allow utility of searching dat, for which we might not need to know on how laptop was created). Transitional knowing, might not allow flexibility, of knowing conformations/structures for the attainment of other utilities (we may not be able to create another laptop/machine, for other utilities, if we don't know the working of the laptop/machine?).
Euler080

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Joined: Tue Nov 06, 2018 8:14 am

### Re: Negative consequences on knowing data transitionally

Could you clarify what you mean by data here? It seems at though you either have one data point (e.g. a single measurement of a quantity), or you have a time series of data points (e.g. the same measurement at different times). In either case they are "historical" in the sense that they are measured at some specified time in the past.

So, one way to phrase your question is in terms of the number of data points in your series, and the time period they cover: if we have 10 data points, is it better to have them spread over a larger period, e.g. once a year for the past 10 years versus once a week for the past 10 weeks?

And if that's what you're asking, then I'd say it depends on what you're measuring and what insight you're trying to gain. Different measured values have different cycles, and different volatility on different scales. Measuring temperature at my desk once a minute for 10 minutes will tell me less about long term trends in global warming, but it might tell me about how my computer is running; compare with measuring the temperature at the bottom of the Gulf of Mexico once a decade for the past century.

I agree with you the current state of any measurement can be misleading, but that seems like more a problem of too little data than which data. And for which data, there doesn't seem to be an objective preference for older data over more recent for all purposes.
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Carleas
Magister Ludi

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