ON METHOD: AN ESSAY
Edward E. Rochon
Edward E. Rochon on Shakespir
On Method: An Essay
Copyright © 2017 by Edward E. Rochon
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Some Other Works by the Author
[Axioms & Theorems: An Essay
Cubics: A Numbers Essay
EMF Banding Model
Ethereal Mea Culpa
Global Warming: An Essay
God & Square Roots
God & Square Roots II
Holographic TV: An Essay
The JU Engine
Pest Control: An Essay
Pollution Solution: An Essay
Pollution Soup Cook: An Essay
Super Intelligence: An Essay]
Table of Contents
We have drugs entering the marketplace of dubious effectiveness and/or outright dangerous. Nonsense pollutes the world of physics and the empirical sciences. Mathematics is corrupted in the same way. Some clever tricky equation that must clearly be false is hailed as truth. Why? Because the equation is the demonstrable proof when it is not. Everything is proven by Einstein, when nothing is proven by Einstein. Well, it is logic, deductive reasoning that proves the validity of science, not your stupid misinterpretation and distortion of data. There is no proof that validates lies and nonsense.
Francis Bacon held a great many erroneous ideas about nature and science, as did Voltaire. The so called Age of Enlightenment should have been more properly called the Age of Benightedness. Under a veneer of rationality and pursuit of truth, the whole tenor of European science was corrupted, slowed down, not speeded up. Yet, these morons take claim for its acceleration. For my part, I hold that Newton did not invent the calculus. He claimed Leibniz stole his ideas. Leibniz claimed Newton stole it from him. The kangaroo court of the Royal Society, completely dominated by Newton decided in favor of Newton. Leibniz received no fair hearing. We know that Leibniz in his early life was not proficient in mathematics and that he inspected the paperwork notes of Descartes. Descartes claimed that he left many things unsaid to give others the chance of working them out for themselves. Descartes made his Geometry hard to figure out, because he had an aversion to fame. He also told the Queen of Sweden that he was near to discovering the secret of eternal life. So he had bigger fish to fry. Leibniz got calculus from Descartes (probably pocketed some of the notes) and offered to develop it with Newton. Newton, an extremely jealous, vain man, was furious, as he and mathematicians from Fermat to all major European thinkers were tinkering with the problem. Newton stole the outline from Leibniz who stole it from Descartes. That is, he did not do what Descartes wanted, figure it out for himself, but purloined it from Descartes.
If you expect me to believe that Descartes had not worked with solid analytical geometry, did not see the connection between his geometry and dealing with motion, I say: ha, ha, ha, ha. Newton claimed to have no interest in fame but merely motivated by the search for truth. His actions throughout life make this claim a lie. He was a jealous, vainglorious scoundrel. He refrained from quickly publishing works, because of his great fear of making mistakes. He could not tolerate criticism. He ostracized, he and his crowd, Leibniz from England when the Elector of Hanover took the British crown. There is some suspicion that Leibniz was murdered by poisoning. If you think the mercurial Newton (40 times normal levels of mercury in his hair according to modern laboratory test: an alchemist also looking for the philosopher’s stone to make gold and give eternal life) lacked the murderous, vindictive instinct to off Leibniz, you have not read the biography of Newton in detail. The man was a scoundrel in a nation of scoundrels. It is not called Perfidious Albion without reason. Francis Bacon was a scoundrel before him. Descartes was a bad physicist for the most part but a fair mathematician. I am convinced that he had already worked out solid analytical geometry and the rudiments of calculus, but left it to others to flush these out for his peculiar reasons. By the way, Henri Poincaré developed the famous equation of Einstein before Einstein as well as the Relativity equations. Unlike Einstein, Poincaré plainly stated that these ridiculous equations were merely algorithms to work out current problems in subatomic particle physics and the Michelson-Morley Effect. Poincaré shunned publicity; Einstein basked in it. Even if Einstein worked them out on his own, he was still a second place also ran. And his claim that these equations represented reality is utterly contemptuous. So France through Poincaré is responsible for these great formulations (accepting the nonsense that culture and not men create and invent.)
By the way, Newton’s so called laws of nature are not laws of nature. They are merely algorithms that work within specified parameters. They cannot possibly be absolutely true. Not only do we have the problem of Mercury’s orbit, the late astronomer Thomas Jefferson Jackson See claimed that the Moon itself has minute perturbations that are at variance with Newton’s equations. I have tried to determine if any modern astronomy has refuted the existence of these variations, but have found no evidence. This does not mean they have not been refuted, but I am suspicious. And spare me any crap nonsense of about General Relativity. This is not and cannot possibly explain anything, being lies and nonsense. Now if the Moon deviates from the formulations, what about even smaller variations among the planets that may exist but too minute to easily detect? See was hated by the Einstein crowd and accused of shoddy workmanship and observations. In the past, his claim to have discovered the first planet around a star was supposedly disproven. Now in this century, the planet has been confirmed. Between Einstein and See, it is Einstein who is the fool and liar. Einstein deserved the criticism of See, that was largely correct, but the scumbags of MIT and Cambridge have suppressed the truth. Now, I make no affirmation of See’s own notions of gravitation, other than to strongly support his view that gravitation should be considered a mechanical or push force with the means of the push still subject to discovery, investigation and a reasonable algorithm of explanation, indicating a highly likely proof that that is how the heavens operate. Modern physics is utterly disgusting and a great impediment to the advancement of truth. When will the world sicken of non-existent 10th and 11th and 4th dimensions, pseudo-math and pseudo-science? How much more nonsense of space expanding and contracting into what has no place to expand or contract into?. How much more: it is all proven by Einstein, from crap psychology, to New Age nonsense, thinking clocks, thinking particles, zero mass mass? Oh, please, Divine Wisdom, wave your hand before the eyes of idiots and make them see.
Chapter 1: Logic of Method
The scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. To be termed scientific, a method of inquiry is commonly based on empirical or measurable evidence subject to specific principles of reasoning. The Oxford Dictionaries Online define the scientific method as “a method or procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses”. Experiments need to be designed to test hypotheses. Experiments are an important tool of the scientific method.
Wikipedia: Scientific Method
It is virtually impossible to discern any conclusive theory or all encompassing algorithm that is not discerned in the gestalt, that is, the phenomenon is studied in the totality of its inertial framework. This refers to empirical science, not mathematics or some purely logical topic.
For any phenomenon in an inertial framework dependent upon known factors: a, b, c, d, e, we cannot exclude unknown factors: f through z. Since the history of science over these thousands of years forever brings new and hitherto unknown matters to light, this is not will o’ the wisp speculation. Moreover, even when dealing with known factors, these factors are put in a controlled environment that must perforce distort the data derived at. And data never tells us anything except by logical analysis. Analysis with limited and suspect and distorted data has great limitations. Of course, the astute man uses logic to try and eliminate these distortions through reason, and to divine new unknown factors that are skewing his results from what he expects.
Discerning things in gestalt has great difficulties attached to it. The subjects and objects of the investigation set is enormous in size. Trying to isolate factors that skew or alter results is quite difficult. So we resort to controlled experiments that are at the heart of the scientific method.
It is generally considered bad form to selectively choose data that gives you the results that you want, because that is not “scientific.” In point of fact, the so called scientific method is hardly scientific. It is simply what the ignorant do when unable to form a cohesive explanation of things by deductive reasoning and by imaginative induction that forms a theory or algorithm. But the scientific method is an idol, a Baconian idol, that must not tumble or be knocked over. If you prefer, you can blame it on the French and call it a Cartesian idol.
Given the limitations of controlled experimentation, it would be well advised to force the data set to get what you expect to find, the object of your investigation. This is the most economical method of getting results from the viewpoint of money, assets, manpower and labor time. Forcing the data set means whittling the set of subjects or objects on the basis of the best early results of testing. I will explain why this is the best method in the next chapter.
Chapter 2: Forcing Data
The most high profile use of the scientific method is with medical testing of medications and allopathic procedures. Medicine is a particularly difficult area for the scientific method, even more so with psychotropic medications (mental illness.) It is generally considered bad form to selectively pick your sample recipients for testing. To deal with diabetes, you would of course selectively choose diabetics. However, you could also selectively pick non-diabetics as well. Perhaps, the wonder pill will help deter diabetes, or you simply want to compare diabetics with non-diabetics for a more comprehensive data set of results. The non-diabetics mean more trouble, money, time, etc.
Results are important for both profit and non-profit science. Everyone has a budget to live within, limited personnel and facilities. And we all want to excel by getting more stuff, inventions stuff, pills stuff, methods stuff under the belt to shine when investment time and honors time come around.
But the idol is hardly all that scientific, and truth is about getting results to be wise, to be healthy, strong and capable. The results are what matter, the method only useful to the extent that it serves the fulfillment of the results.
You can gather a mass of statistical data that covers as much of the subjects or objects involved without any controls other than that the data may be related to some specific parameter: people with cancer, etc. This adds to expense, but may be a good idea to get the best results. You pick out a sample of humanity or of the object set. You have a hypothesis or theory to be explored. You want the results desired to be correct or successful. Watch early indicators of success in the sampling. Eliminate those that are not manifesting the desired results. Eliminate those who show results but not the best results. Doing this focuses investment, labor and time on those candidates that have the desired effect to the extent that it is profitable as a cure, treatment, test, extraction of high quality, etc. Concentrate on what works. The whole foundation of the scientific method as currently professed is flawed. You want results, not distortions, but still results.
Study your small sample intently. Its small size allows easier correlation of similarities that make the process work, and also easier identification of differences. These differences may be irrelevant (likely) or contributory by diverse means (differences aid the process by differing means.) This is the best way to find out why your hypothesis works, whether on your assumption totally, or partially, or serendipitously. Do not waste resources.
If you are looking for a medication, you at least know that a fraction of the population will benefit from it, and can offer physicians check sheets to look for successful candidates. If the disease is very hurtful, you should be able to charge enough to cover overhead against a limited clientele. At least you have something, and do not have to listen to so many complaints that the medication is worthless, ineffective. Now that you have a potential income flow, the method of forced data does not stop there.
Form a statistical bullseye with the core successful cures or treatments at the center of the bullseye. Put less successful candidates in zones around the bullseye from more to less successful. With a good idea of how your drug works on the core group, try and figure out what is interfering with the less successful groups. Call back subjects if possible or get roughly equivalent candidates based on your description set. Go from what you know works to what you do not know in this fashion. Forcing the data is more likely to give you less distorted results than with going through an entire sampling from beginning to end, and will be more cost effective in the end. Your work will explain discrepancies in results more effectively. As all medical and scientific work is related to varying degrees, this method is best to get around the inevitable distortion that comes from controlled sampling and testing. And if your core sample cannot show results, why waste anymore time, this the ideal test set?
It is apparent from various exposés of pharmaceutical companies and university research that frustrated researchers do this anyway, but dishonestly to a great degree, haphazardly and expensively. When the money is on the line, results are desired. Are the results real or illusory through doctored results? If the boss is going to be pissed off with you anyway, force the data early to cut back on income hemorrhaging due to disappointing results. Sorry, Francis, René, you ‘is’ dumb as the practical Hillbilly would say.
Other Works by the Author
Elements of Physics: Matter
Elements of Physics: Space
Elements of Physics: Time
Space as Infinity: An Essay
Space as Infinity II: An Essay
Unified Field Theory: An Essay
Collected Poems I
Collected Poems II
Golden Age Essays
Golden Age Essays II
Golden Age Essays III
Golden Age Essays IV
Golden Age Essays V
My current biography and contact links are posted at . My writings include essays, poetry and dramatic work. Though I write poetry, my main interest is essays about the panoply of human experience and knowledge. This includes philosophy, science and the liberal arts. Comments, reviews and critiques of my work are welcome. Thank you for reading my book.
A brief preface attacks modern scientific methods and icons of science such as Newton and Einstein. The scope of the essay is made. Chapter 1 suggests that gestalt observation of the problem is the best way to get undistorted test results. It is virtually impossible not to distort data results when testing outside of the real world inertial framework, except for mathematics and logic problems. There are always unknowns that cannot be accounted for and that will alter data results outside of the domain of the hypothesis under examination. Chapter 2 shows forced set method. Try and whittle the subjects or objects of the data set to get what you want. Limiting the set cuts back on expense in money and time. If the most opportune set will not confirm your results, drop the hypothesis. A small successful result will offer some opportunity to recover expenses in for profit industry, and validate pure research. Once the core sample is successful within acceptable limits, study the similarities and differences minutely. Surround the core in a bullseye fashion with rings of data from more successful to less. (For example, if the core is 99% successful, and the outer sample 10% successful, with the utmost virtually 0% successful, use the core sample analysis to determine why these samples are not up to par.) By going from known to unknown, you may be able to find the unknowns that turn 10% success to 99% success. Nobody wants to be wrong. Force the data set to get what you want right off the bat. This is not bad science but good economy of effort.