"Perceptions" ITEM
Copyright © 2009 Ray Dickenson

Methods of Inquiry

1 - the first step is used by trouble-shooters and is a straight checking of a closed system's actions. At its simplest it's asking "YES" or "NO" of all processes or decisions, with no exclusions.

John le Carré's `George Smiley' used the same method, only based on paper-records, to find the moles in MI-6* (S.I.S).
http://en.wikipedia.org/wiki/George_Smiley

[Although geeks might call it `Boole's Logic', when they're checking more complex digital systems. http://en.wikipedia.org/wiki/Boolean_algebra_(logic)
http://en.wikipedia.org/wiki/Truth_table#Applications_of_truth_tables_in_digital_electronics ]

I.e. - basically it's collecting the data - on what the system does, and when.

TO EVALUATE THE SYSTEM YOU NEED TO TAKE ANOTHER STEP

2 - the next step is quantitative statistical analysis of the results you got from (1). This is the method used by insurance companies, political parties & some gov't agencies - it's often called `The Law of Large Numbers'.
http://en.wikipedia.org/wiki/Statistics

Though not applicable to individuals, it's a powerful predictor and analyzer of the reactions of populations, and also of the declared outcomes of large organizations.

Warning - league tables of hospitals *, education systems *, police forces etc, claim to show the relative efficiencies of those systems. Mostly, that's not true - as seen below.

REALITY CAN BE SHOWN BY ONE MORE STEP

3 - the last step is qualitative or ontological analysis of the results you got from (2).

That is, double-check claimed or raw data outcomes against those predicted (demanded) by the `Law of Large Numbers'.

Because outcome results can lie, especially when viewed as `raw data'.

An example is at `examination bias'.

As that example shows, any mis-match is a sure indication of corruption of some sort.

SUMMING UP

step (1)
data - a system's actions or claimed actions;

step (2)
indications - or outcomes & claimed efficiency;

step (3)
solutions - and whether the system is honest or corrupt.

As you might guess, politicians lawyers and judges (+ media) try to limit inquiry to step (1), maybe to hide inefficiency and hide corruption.
Examples - gov't criminals, fake fines, crapbbc, faked IFOs, sci-gov cover-ups

And some police/security, scientists and bureaucrats try to restrict analysis to step (2), maybe to favor `selective' data, i.e. to hide bias and/or corruption.
Examples - `examination bias' and `violence check'

FREE WILL?

TRUTH REAL?

ANALYSIS

`Perceptions'
DETECTION

EQUATION

WHY NOT BELIEVE STATISTICS?

CAUTION
`Categorizing' is a useful tool - when used after the analysis phase of a scientific investigation.

However, folk who've gotten opinionated (over-impressed by its apparent efficiency) will sometimes apply categorizing at the initial phase of data gathering. As a result they might declare `That's impossible / a hoax!' before analysis of data.

That fatal `pre-judging' is a typical weakness of older or more dogmatic scientists - see Clarke's First Law

Here's some famous examples of eminent scientists making fools of themselves - by pre-judging the data, or categorizing before analyzing.