The Ethical Skeptic

Challenging Pseudo-Skepticism, Institutional Propaganda and Cultivated Ignorance

Critical Attributes Which Distinguish the Scientific Method

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 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 one or more of these critical attributes to be skipped, in an effort to get to a desired answer destination in as expedient a manner as is possible – yet still appear as science.

The Critical Attributes of Science

My identification of the critical attributes of science, and especially the early and neglected part of the scientific method, are reflected by 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.”


Although science is indeed an iterative process, nonetheless true science, as opposed to general or developmental study, involves these critical path steps at the beginning of the scientific method:

  1. Observation
  2. Intelligence
  3. Necessity

Science thereafter is an iterative method which bears the following necessary features:

  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, 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 exclusive club lab activity.  Finally, incorporating their corrupted version of ‘peer review’ wherein they seek to kill ideas before they can be formulated into a hypothesis and be studied. This is a process of corruption.

Most unanswered questions reside in a state of quandary precisely because of a failure in or refusal to pursue 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 sometimes slips by as valid to political agenda proponents and the mainstream press – that method which is practiced in the pesticide and vaccine industries.  It follows:

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 nor frame the core problem definition, nor the data pulled, nor the methodology/architecture of study.

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 systematic review, randomized controlled trial, cohort study, case-control study, cross-sectional study, case reports and series, or reports 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 felled 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/salience of the contained measures themselves.

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 (our 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 a 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. 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.

epoché vanguards gnosis

How to MLA cite this blog post =>
The Ethical Skeptic, “The Scientific Method Contrasted with The Experimental Method” The Ethical Skeptic, WordPress, 31 March 2018, Web;
  1. Paul Humphreys,”The Oxford Handbook of Philosophy of Science; Oxford University Press, New York, NY, 2016; pp.284-5.

March 31, 2018 - Posted by | Ethical Skepticism | , ,

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