The scientific method bears several critical attributes which distinguish it from both mere experiment, as well as its masquerade, the Lyin’tific Method. It behooves the ethical skeptic to understand the critical features which distinguish science from its pretense; to maintain the skill in spotting social manipulation in the name of science.

The experimental method is a subset of the scientific method. There exists a distinct difference between these two protocols of science. The experimental method is oriented towards an incremental continuation of existing knowledge development, and accordingly begins with the asking of a question, bolstered by some quick research before initiating experimental testing. But not all, nor even the majority of knowledge development can be prosecuted in this fashion of study. Under the scientific method, one cannot boast about possessing the information necessary in asking a question at the very start. Asking an uninformed question may serve to bias the entire process – or kill the research artificially without the full awareness of the sponsors or stakeholders. Accordingly, in the scientific method, a question is not asked until step 8 – this in an effort to avoid the pitfalls of pseudo-theory. This is purposeful, because the astute researcher often does not know the critical path necessary to reach his discovery – at the very beginning. Science involves a intelligence development period wherein we ask, 1. what are the critical issues that need to be resolved, 2. what are the irrelevant factors we can ignore for now? and 3. how do I chain these issue resolutions into a critical path of knowledge development? In absence of this process, there exists a bevy of questions – wherein just selecting one and starting experiments, is akin to shooting in the dark. The Oxford Handbook of Philosophy of Science expounds on such a mistake thusly:

The materials physicist Percy Bridgman, commented upon the process by which we ‘translate’ abstract theories and concepts into specific experimental contexts and protocols. Calling this work of reduction and translation ‘operationalism’ – Bridgman cautioned that experimental data production is often guided by substantial presuppositions about the subject matter which arise as a part of this translation. Often raising concern about the ways in which initial questions are formulated inside a scientific context. True science is a process which revisits its methodological constructs (modes of research method) as often as it does its epistemological (knowledge) ones. Accordingly, this principle identified by Bridgman is the foundation of the philosophy which clarifies the difference between the scientific method, and the experimental method. It is unwise to consider the two as being necessarily congruent.¹

The process of developing a scientific question, is many times daunting, involving commitment from a sponsor, a long horizon of assimilating observational intelligence and persistence in seeking to establish a case for necessity. A necessity which serves to introduce plurality of argument (see Ockham’s Razor), which can be brought before peers. Advising peers who are in support of the research and assist in developing the construct being addressed, into a workable hypothesis. These peers are excited to witness the results of the research as well.

Science is a process of necessity in developing taxonomic observation, which seeks to establish a critical path of continuously evaluated and incremental in risk conjecture, probing by means of the most effective inference method available, the resolution of a query and its inevitable follow-on inquiry, in such manner that this process can be replicated and shared with mankind.

The Lyin’tific Method in contrast, will target skipping one or more of these critical attributes of science, in an effort to get to a desired answer destination in as expedient a manner as is possible – yet still appear as scientific methodology.

The most anti-science position one can adopt is the insistence that the scientific method consists of one step: 1. Proof.

The Critical Attributes of Science

My identification of the critical attributes of science, and especially the early and neglected steps of the scientific method, are reflected inside a statement on the part of the fictional character Sherlock Holmes, from the 1887 novel, A Study in Scarlett by Sir Arthur Conan Doyle.

“It is a capital mistake to theorize before you have all the evidence.
It biases the judgment.”

Therefore,

Although science is indeed an iterative process, nonetheless true scientific inquiry, as opposed to technical or developmental study, involves these critical path steps which reside at the beginning of the scientific method. Steps which are critical in avoiding the mistake cited by Sir Arthur Conan Doyle above:

Observation
Intelligence
Necessity

Science thereafter is an iterative method which bears the following necessary features (see related also The Elements of Hypothesis):

1.  Flows along a critical path of dependent, salient and revelatory observation and query
2.  Develops hypothesis through testable mechanism
3.  Is incremental in risk of conjecture (does not stack conjectures)
4.  Examines probative study in preference over reliable data
5.  Seeks reliable falsification over reliable inductive inference
6.  Seeks reliable consilience over reliable abductive inference
7.  Does not prematurely make a claim to consensus in absence of available deduction
8.  Shares results, limitations, open issues, next questions, next steps and replication guidance.

Social skeptics seek to deny the first three steps of science, along with routinely ignoring its necessary features. Social skeptics then further push the experimental method in place of the above attributes of science – asking a biased and highly uninformed question (also known in philosophy as rhetoric), while promoting science as nothing but an exclusive club or lab activity.  Finally, incorporating their corrupted version of ‘peer review’ (at sponsorship, see below) wherein they seek to kill ideas before they can be formulated into a hypothesis and be studied. This is a process of corrupted philosophy of science – corrupt skepticism.

Most unanswered questions reside in a state of quandary precisely because of a failure in or refusal to pursue and apply the above characteristics of science.

Accordingly, the scientific method begins with a process of circumspection and skepticism, which is distinctly different from the inception of the much more tactical experimental method. To scoff at this distinction, reveals a state of scientific illiteracy and of never having done actual scientific research nor discovery.

While both the experimental method and the scientific method are valid process descriptions applicable to science, there does exist an abbreviated version of the scientific method which is errant precisely because it adheres only to the Experimental Method. A method which is exposed and vulnerable to Conan Doyle’s caution about presuming which question to ask, or which theory to prosecute, before enough intelligence and necessity has been assimilated:

sciebam

/philosophy : appeal to authority : pseudoscience/ : (latin: I knew) – an alternative form of knowledge development, which mandates that science begins with the orphan/non-informed step of ‘ask a question’ or ‘state a hypothesis’. A non-scientific process which bypasses the first steps of the scientific method: observation, intelligence development and formulation of necessity. This form of pseudoscience/non-science presents three vulnerabilities:

First it presumes that the researcher possesses substantially all the knowledge or framework they need, lacking only to fill in final minor gaps in understanding. This creates an illusion of knowledge effect on the part of the extended domain of researchers. As each bit of provisional knowledge is then codified as certain knowledge based upon prior confidence. Science can only progress thereafter through a series of shattering paradigm shifts.

Second, it renders science vulnerable to the possibility that, if the hypothesis, framework or context itself is unacceptable at the very start, then its researcher therefore is necessarily conducting pseudoscience. This no matter the results, nor how skillfully and expertly they may apply the methods of science. And since the hypothesis is now a pseudoscience, no observation, intelligence development or formulation of necessity are therefore warranted. The subject is now closed/embargoed by means of circular appeal to authority.

Finally, the question asked at the beginning of a process of inquiry can often prejudice the direction and efficacy of that inquiry. A premature or poorly developed question, and especially one asked under the influence of agency (not simply bias) – and in absence of sufficient observation and intelligence – can most often result quickly in a premature or poorly induced answer.

And sciebam, as a quasi-scientific method would almost be fine in itself, if not for the specter of agency which then further exploits even that rogue approach to science into a complete and permissive masquerade; through additional methodology as outlined below. Such is the methodology practiced by the Pesticide and Vaccine industries today. False science, which can be identified by its faux methodology, all dressed up in profit laden and corporate lab coats.

Ladies and gentlemen, the Lyin’tific Method.

The Lyin’tific Method: The Ten Commandments of Fake Science

When you have become indignant and up to your rational limit over privileged anti-science believers questioning your virtuous authority and endangering your industry profits (pseudo-necessity), well then it is high time to undertake the following procedure.

1. Select for Intimidation. Appoint an employee who is under financial or career duress, to create a company formed solely to conduct this study under an appearance of impartiality, to then go back and live again comfortably in their career or retirement. Hand them the problem definition, approach, study methodology and scope. Use lots of Bradley Effect vulnerable interns (as data scientists) and persons trying to gain career exposure and impress. Visibly assail any dissent as being ‘anti-science’, the study lead will quickly grasp the implicit study goal – they will execute all this without question. Demonstrably censure or publicly berate a scientist who dissented on a previous study – allow the entire organization/world to see this. Make him become the hate-symbol for your a priori cause.

2. Ask a Question First. Start by asking a ‘one-and-done’, noncritical path & poorly framed, half-assed, sciencey-sounding question, representative of a very minor portion of the risk domain in question and bearing the most likely chance of obtaining a desired result – without any prior basis of observation, necessity, intelligence from stakeholders nor background research. Stress that the scientific method begins with ‘asking a question’. Avoid peer or public input before and after approval of the study design. Never allow stakeholders at risk to help select or frame the core problem definition, nor identify the data pulled. Never allow a party highly involved in making observations inside the domain (such as a parent, product user or farmer) to have input into the question being asked nor the study design itself. These entities do not understand science and have no business making inputs to PhD’s.

3. Amass the Right Data. Never seek peer input at the beginning of the scientific process (especially on what data to assemble), only the end. Gather a precipitously large amount of ‘reliable’ data, under a Streetlight Effect, which is highly removed from the data’s origin and stripped of any probative context – such as an administrative bureaucracy database. Screen data from sources which introduce ‘unreliable’ inputs (such as may contain eyewitness, probative, falsifying, disadvantageous anecdotal or stakeholder influenced data) in terms of the core question being asked. Gather more data to dilute a threatening signal, less data to enhance a desired one. Number of records pulled is more important than any particular discriminating attribute entailed in the data. The data volume pulled should be perceptibly massive to laymen and the media. Ensure that the reliable source from which you draw data, bears a risk that threatening observations will accidentally not be collected, through reporting, bureaucracy, process or catalog errors. Treat these absences of data as constituting negative observations.

4. Compartmentalize. Address your data analysts and interns as ‘data scientists’ and your scientists who do not understand data analysis at all, as the ‘study leads’. Ensure that those who do not understand the critical nature of the question being asked (the data scientists) are the only ones who can feed study results to people who exclusively do not grasp how to derive those results in the first place (the study leads). Establish a lexicon of buzzwords which allow those who do not fully understand what is going on (pretty much everyone), to survive in the organization. This is laundering information by means of the dichotomy of compartmented intelligence, and it is critical to everyone being deceived. There should not exist at its end, a single party who understands everything which transpired inside the study. This way your study architecture cannot be betrayed by insiders (especially helpful for step 8).

5. Go Meta-Study Early. Never, ever, ever employ study which is deductive in nature, rather employ study which is only mildly and inductively suggestive (so as to avoid future accusations of fraud or liability) – and of such a nature that it cannot be challenged by any form of direct testing mechanism. Meticulously avoid direct observation, randomized controlled trial, retrospective cohort study, case-control study, cross-sectional study, case reports and series, or especially reports or data from any stakeholders at risk. Go meta-study early, and use its reputation as the highest form of study, to declare consensus; especially if the body of industry study from which you draw is immature and as early in the maturation of that research as is possible.  Imply idempotency in process of assimilation, but let the data scientists interpret other study results as they (we) wish. Allow them freedom in construction of Oversampling adjustment factors. Hide methodology under which your data scientists derived conclusions from tons of combined statistics derived from disparate studies examining different issues, whose authors were not even contacted in order to determine if their study would apply to your statistical database or not.

6. Shift the Playing Field. Conduct a single statistical study which is ostensibly testing all related conjectures and risks in one fell swoop, in a different country or practice domain from that of the stakeholders asking the irritating question to begin with; moreover, with the wrong age group or a less risky subset thereof, cherry sorted for reliability not probative value, or which is inclusion and exclusion biased to obfuscate or enhance an effect. Bias the questions asked so as to convert negatives into unknowns or vice versa if a negative outcome is desired. If the data shows a disliked signal in aggregate, then split it up until that disappears – conversely if it shows a signal in component sets, combine the data into one large Yule-Simpson effect. Ensure there exists more confidence in the accuracy of the percentage significance in measure (p-value), than of the accuracy/precision of the contained measures themselves. Be cautious of inversion effect: if your hazardous technology shows that it cures the very thing it is accused of causing – then you have gone too far in your exclusion bias. Add in some of the positive signal cases you originally excluded until the inversion effect disappears.

7. Trashcan Failures to Confirm. Query the data 50 different ways and shades of grey, selecting for the method which tends to produce results which favor your a priori position. Instruct the ‘data scientists’ to throw out all the other data research avenues you took (they don’t care), especially if it could aid in follow-on study which could refute your results. Despite being able to examine the data 1,000 different ways, only examine it in this one way henceforth. Peer review the hell out of any studies which do not produce a desired result. Explain any opposing ideas or studies as being simply a matter of doctors not being trained to recognize things the way your expert data scientists did. If as a result of too much inherent bias in these methods, the data yields an inversion effect – point out the virtuous component implied by your technology – how it will feed the world or cure all diseases, is fighting a species of supremacy or how the ‘technology not only does not cause the malady in question, but we found in this study that it cures it~!’.

8. Prohibit Replication and Follow Up. Craft a study which is very difficult to or cannot be replicated, does not offer any next steps nor serves to open follow-on questions (all legitimate study generates follow-on questions, yours should not), and most importantly, implies that the science is now therefore ‘settled’. Release the ‘data scientists’ back to their native career domains so that they cannot be easily questioned in the future.  Intimidate organizations from continuing your work in any form, or from using the data you have assembled. Never find anything novel (other than a slight surprise over how unexpectedly good you found your product to be), as this might imply that you did not know the answers all along. Never base consensus upon deduction of alternatives, rather upon how many science communicators you can have back your message publicly. Make your data proprietary. View science details as an activity of relative privation, not any business of the public.

9. Extrapolate and Parrot/Conceal the Analysis. Publish wildly exaggerated & comprehensive claims to falsification of an entire array of ideas and precautionary diligence, extrapolated from your single questionable and inductive statistical method (panduction). Publish the study bearing a title which screams “High risk technology does not cause (a whole spectrum of maladies) whatsoever” – do not capitalize the title as that will appear more journaly and sciencey and edgy and rebellious and reserved and professorial. Then repeat exactly this extraordinarily broad-scope and highly scientific syllogism twice in the study abstract, first in baseless declarative form and finally in shocked revelatory and conclusive form, as if there was some doubt about the outcome of the effort (ahem…). Never mind that simply repeating the title of the study twice, as constituting the entire abstract is piss poor protocol – no one will care. Denialists of such strong statements of science will find it very difficult to gain any voice thereafter. Task science journalists to craft 39 ‘research articles’ derived from your one-and-done study; deem that now 40 studies. Place the 40 ‘studies’, both pdf and charts (but not any data), behind a registration approval and $40-per-study paywall. Do this over and over until you have achieved a number of studies and research articles which might fancifully be round-able up to ‘1,000’ (say 450 or so ~ see reason below). Declare Consensus.

10. Enlist Aid of SSkeptics and Science Communicators. Enlist the services of a public promotion for-hire gang, to push-infiltrate your study into society and media, to virtue signal about your agenda and attack those (especially the careers of wayward scientists) who dissent.  Have members make final declarative claims in one liner form “A thousand studies show that high risk technology does not cause anything!” ~ a claim which they could only make if someone had actually paid the $40,000 necessary in actually accessing the ‘thousand studies’. That way the general public cannot possibly be educated in any sufficient fashion necessary to refute the blanket apothegm. Have them demand final proof as the only standard for dissent. This is important: make sure the gang is disconnected from your organization (no liability imparted from these exaggerated claims nor any inchoate suggested dark activities *wink wink), and moreover, who are motivated by some social virtue cause such that they are stupid enough that you do not actually have to pay them.

To the media, this might look like science. But to a life-long researcher, it is nowhere near valid.  It is pseudo-science at the least; and even worse than in the case of the paranormal peddler – it is a criminal felony and assault against humanity. It is malice and oppression, in legal terms.

The discerning ethical skeptic bears this in mind and uses it to discern the sincere researcher from the attention grabbing poseur.

The Ethical Skeptic, “Critical Attributes Which Distinguish the Scientific Method”; The Ethical Skeptic, WordPress, 31 Mar 2018; Web, https://wp.me/p17q0e-7qG