Some time ago, I had a conversation with an elderly friend of mine who told me that until the mid-1960s, people would not
lock their houses. I find such a thing difficult to imagine in today's society, especially after all the efforts of politicians, humanists and sociologists to implement a
state of the art democracy. I enquired from others who remember that time and they confirmed that indeed, back then, people did not lock their houses. Thus, through the
testimony of several trustworthy elderly people who had very little interaction with each other and lived in three very different, distinct European countries, I accept
for an established fact that, until the mid-1960s, people did not lock their houses and cars. Furthermore, it also seems that in the past, people were happier and healthier.
The latter might contradict what the media usually states, but one could simply look at the life span of people in an old graveyard or the life span of famous scientists,
artists, musicians, etc. from the past and make his/her own mind. It seems that if a person had a relatively decent lifestyle (a counter example is Schubert who died from
syphilis at age of 31) or hadn't had some unfortunate accident due to which they would prematurely die, most people would have lived to their seventies and later. So if
society is getting better, as the politicians and media claim, why is it obviously getting worse? Similarly, if there are always new and better drugs – why are people getting
sicker? In this article I will look at one of the reasons for this misunderstanding. But first let us look at some definitions, namely what science is:
A branch of knowledge or study dealing with a body of facts or truths systematically arranged and showing the operation of general laws: the mathematical sciences.
A branch of knowledge based on objectivity and involving observation and experimentation.
systematically acquired knowledge that is verifiable.
Those branches of study relating to the phenomena of the physical universe and its laws, a connected body of demonstrated truths with observed facts systematically classified under general laws; the study of relative, modified Principles which can be proven through physical measurements and ...
A method of gathering information through the senses and logic (mathematics). Science has origins in philosophy. Science is one of humanity's inventions. But science as a method is more specific than philosophy.
I claim that statistics and probability (from which statistics derives number of concepts) are unscientific, self-serving
methodologies that yield results which are meaningless and unattached to objective reality. As such, these results can be (and usually are) exploited in ways the party using
statistics and probability finds fittest for achieving his/her own particular objectives. Which explains the above observation that what people are told does not match what is
observable. We shall now scrutinize the statistical and probabilistic paradigm and either confirm or refute their scientific nature.
1. Philosophical incompatibility with science.
Science is based on two beliefs, philosophical understandings, assumptions or principles that (first) the Universe is ordered, i.e. that it is both governed and
driven by laws, which (second) are intelligible to us humans. Neither the order of the world, nor the intelligibility of its
laws has to be so, nor can either of them be proven by us. We cannot prove that the world is ordered or intelligible, as we cannot prove that whatever we see and understand from,
and about it, is indeed so. However we believe that the world is ordered and we also believe that it is intelligible. This belief is the foundation
of science.
For example, physics and mathematics base themselves on this notion and as a result they produce laws about the Universe. Statistics and probability on the other hand do not study,
understand or present intelligible laws with a clearly specified type, semantics or boundaries that are in any way related to the Universe. Whenever faced with any particular problem,
statistics and probability always starts with one or both of the following:
1. there is no law that governs the matter, thus denying the first of the two fundamentals principles of science that the world is ordered,
2. there is a law that governs the matter but we cannot formalize it, which is denial of the second fundamental principle of science that laws governing the world are intelligible,
then they proceed using a mechanical aggregation of some (whatever) available data, which may or may not be adequately related to the examined matter. Thus by its nature, statistics
and probability are unscientific because they deny one or both of the fundamental principles of science which again are that the world is ordered (law for everything) and intelligible
(we understand all laws).
Therefore, as the basis of science is the above philosophy, and as statistics and probability clearly contradict these fundamental principles
of science, it is necessary to conclude that statistics and probability are clearly NOT scientific subjects. They certainly
use scientific notation, which may be necessary, but is certainly insufficient condition for something to be considered a science. Some particular examples will be presented later in the
article.
It is indeed quite astonishing to note some of the more prosaic examples for the complete philosophical discrepancy of the subjects, i.e. the
term "approximate truth" defined in statistics. Truth, by definition, is either complete and unique, i.e. absolute, or it is not truth at
all. Perhaps you can feel the blow of political correctness?!
2. Fundamental inconsistency.
2.1. Statistics and probability assume that an explicit law (for some matter) does not exist or at least cannot be formalized;
2.2. Although they deny the law of study, they use other laws (e.g. for existence and nature of numbers, operations for additions, subtraction, and so forth, including defining their own laws)
- thus they imply that the world is not coherent, i.e. there are laws for some things and there are no laws for other things. However, an incoherent world is not study-able at all as it is
impossible to establish any foundations, boundaries and so forth;
2.3. Statistics (and to certain extent probability) presents its own conclusion as a true fact i.e. as a law.
Obviously:
- 2.3. contradicts 2.1 unconditionally;
- 2.2. could contradict 2.1 subject of circumstances;
- 2.2. alone implies that statistics and probability themselves cannot exist as a science because no science is possible in an incoherent world.
We are therefore required to conclude that statistics and probability are self-contradictory and void subjects as they
try to study/describe a Universe that is impossible to study by their own assumption.
3. Erroneous and dangerous.
A field where statistics is heavily used is when "proving" the effectiveness of drugs. For example new drug is 90% effective
in 123 cases, old drug is effective ... is the new drug better?
Lets us consider a broken TV set which is taken by the owner to be repaired since its warranty has expired. The person at the
repair shop says that he does not have the scheme of this model, but for this problem he has noticed that if he adds a new part to the TV set the device will start working in
90% of the cases. An intact TV set however works in 100% cases, therefore the fix is somewhat related to the problem but is definitely not really fixing it. The owner however
has no other choice and agrees to have the 90% chance-fix put in place. Indeed, after the "fix", the TV works only for a while because the result of the fix is not only that the broken
part is still broken, but that some other parts of the system (TV set) were placed to function in conditions not optimal for them. In an intact system, all subsystems and elements
are designed to work, and work in optimal conditions. Adding or removing anything destroys that optimal state. As a consequence, after a short period of time the TV set will break
again, simply because one or more overloaded elements will give up. Drawing the line, a system that was damaged in a relatively minor way has become more damaged (possibly severely)
simply because the electric laws were replaced with probabilistic and statistical nonsense and ignorance.
Is it then surprising that people are getting sicker and sicker after being mistreated with drugs always "proved" to be "working"
statistically?! As biological machines (such as humans) are much more complex than a TV set, with many more interrelated parts (at any level), buffers, closed loops, self-adjusting
and self-repairing subsystems, these effects are slower to manifest themselves, however they do exist and obviously do manifest themselves.
Consider a dice – a fair dice should account for a fairly flat distribution for all numbers when rolled - that is similar
amounts of 1s, 2s, 3s, etc. Note that the word should is not correct as its presence there is not proven, but for now we will assume it. For example if I have 6 sided fair
dice and I throw it 60 times I would expect to have about 10 ones, 10 twos, 10 threes, etc. occurred purely by chance. In reality these numbers will not be so perfectly distributed, e.g.
any side may occur a few times more or less than the other sides. If one is to use a unfair dice some of the sides for which the dice is biased will occur somewhat more often than other
sides. Statistics devises a technique known as "Fit Test" through which it claims it is able to prove if a dice is fair or unfair.
However I am able to quite easily create a dice with an embedded micro-controller, battery and mechanics which would perform as follows. From the very first roll of the dice the
microprocessor and software will ensure that the dice sets to 1, on the second roll it sets to 2, on the third to 3, etc., to 6. On the seventh roll the micro-controller will set the
dice again to 1 and so forth, thus this obviously unfair dice will have a perfect distribution. After each 6 rolls, the number of 1s, 2s, 3s, etc. will be all equal. According to the
"Fit Test" a dice with such flat distribution is the "THE PERFECT FAIR DICE", but we know better that it is not, so "Fit Test" and the statistical paradigm has just, once again, failed.
A statistician will now object that the dice above will never roll on the same side in 2 consequent rolls, so he/she can "catch" it. There are two ways to respond:
- The first is that I can easily modify the program running on the micro-controller so that the micro-controller does not interfere on the first roll from any series of rolls, however
it records the side occurred. On the second roll the micro-controller ensures that the side that was drawn on the first roll does not occur. On the third draw it ensures that none of
the previously two draws occurs and so forth. Thus we could have the same side occurring 2 simultaneous times - the last roll from one group of rolls and the first roll from the next
group. Of course this amendment of the software would not work for 3, 4 and more sequential same side rolls. However it is obvious that one is able to place whatever software he/she
wishes that operates in a particular way so that it is not detectable by any "Fit Test". Examples are numerous, I will give just one – a random number generator from 1 to 6 running on
the micro-controller. For a "Fit Test", this dice will be an ordinary fair dice, but we know very well that random numbers in computers DO NOT EXIST, and every random generator although,
seemingly random, is not random at all. Further, what if the random generator is re-seeded via a special radio signal? Or what if the micro-controller engages only in the presence of
special radio signal, thus the proof that the dice is fake can only be made by cutting the dice and examining its internals, passing it through x-rays, or other such true scientific means.
- The other more general way to respond is to request a proof: why should we expect to have 2 or more sequential draws on the same side of the dice? Based on the first fundamental
assumption of science (that the world is ordered), I argue that if one is to roll the same dice in exactly the same way in exactly the same conditions he/she will always get exactly
the same side. As no-one accounts for the exact conditions in the so called roll "by chance", no-one can have any (scientific, logical, or whatever valid) reasons to impose any
particular expectations for the outcome, because none of the experiments (rolls) were actually properly observed.
This later argument obviously totally destroys all on what probability and statistics are based, and are about, and this is what science is about - proper and objective observation
and experimentation, not some wishy-washy fairy tales. Once again we demonstrated that probability and statistics are simply not scientific disciplines.
Suppose two software programmers write two computer programs, both working independently and both attempting to solve the same problem.
One of them does everything perfectly but unfortunately makes a typo in the very last line of his program and mistakenly negates the result, thus always yielding a wrong answer. The
other programmer however makes a mistake in the algorithm, and it happens that his program gives correct results in 95% of the cases. A statistician is asked to make a "Fit Test" and
select the better program. The statistician quickly presents his/her choice - the second - 95% of cases - correct program. The first program is then abandoned and possibly destroyed.
However that is the wrong choice - a typo can be easily fixed, however a wrong algorithm might be impossible fix.
Conclusion
In order to prove something, one must prove that it is always true. However, to disprove it, one needs to demonstrate ONLY ONE instance
where it is wrong. Even in an article as short as this one, we clearly disproved any claim that statistics and probability are sciences. We showed this in three different ways: by
philosophy, by logic and by engineering, using a number of examples. Any one of these, on its own, is a sufficient condition to overthrow any suggestion that statistics and probability
are sciences. Therefore we successfully confirmed the claim stated at the beginning that statistics and probability are most definitely not sciences, but are purely self-serving
methodologies that yield results which are meaningless and unattached to objective reality (in as much as science is objective, based on its fundamental assumptions). The intricate
problem of statistics and probability is that they both simply mangle some numbers, instead of understanding the Universe of Discourse which is what the objective of science is.
Statistics and probability are the politically correct and politically misused pseudo-scientific disciplines utilized to "prove" many
things, including that DDT is safe, pesticides and herbicides are OK, drugs that work XX% will help you, and so on ... but do you remember the fix for the TV set above? I see two
plausible reasons to explain the promotion of these unscientific subjects: 1. political and financial misuse, and 2. inability of some people engaged with science to discern the laws
that they try to discover so they refer to probabilistic and statistical methods instead of working harder or leaving the fame for someone else, someone who has ideas.
|
