Logic

Hubbard’s dissertations on logic mainly concentrated on data analysis and are missing several essential ingredients, as the following will show. His so-called outpoints are all various modes of inconsistency and/or incompleteness of data.

Logic is the subject of valid reasoning. Valid reasoning must be consistent and complete. The paradigm of valid reasoning is mathematics, which is the only subject which is consistent and complete. Validity needs valid data and valid relationships (connections) between the data.

Consistency demands that the ideas, statements, and arguments should connect logically and not contain contradictions or fallacies. Incoherent or contradictory reasoning destroys its validity. Valid reasoning must also adhere to the principle of sufficient reason (every event or fact must have an explanation or cause).

Completeness requires a set of data which is accurate, relevant, contains sufficient contextual information and should come from reliable sources. Reasoning is built upon premises (data), which are statements or assumptions that serve as the foundation for arguments. If the premises are flawed or unsubstantiated, the reasoning will be invalidated.

Validity needs valid data and valid relationships (conclusions) of the data.

The following is a more detailed list of requirements for valid reasoning:

  1. All relevant data should be known (this includes any specifics of time and place).
  2. All the data should be true or factual. (including comparisons, assumptions).
  3. Data are from the right source. (not CNN)
  4. There should be no conflicting data or contradictions in the data.
  5. The sequence of events must be correct.
  6. The relative importance of the data must be shown.
  7. Objectives and actions match the actual purposes of the scene.
  8. Correct inferences, coherent lines of reasoning, free from fallacies.

To spot flawed data and erroneous reasoning the following list may be helpful:

Illogics

  1. Omitted data (time, place etc.)
  2. Irrelevant or incorrectly included data
  3. False data (incorrectly assumed differences, similarities or identities)
  4. Contrary facts
  5. Altered or incorrect sequence of events
  6. Altered importance
  7. Wrong target / wrong source
  8. Fallacious inferences, faulty conclusions

Here is a list of some common logical fallacies:

  1. Ad Hominem: Attacking the person making the argument rather than addressing the argument itself.  Altered importance.
  2. Straw Man: Misrepresenting or distorting someone’s argument to make it easier target to attack. Named after the practice of setting up a straw-stuffed scarecrow as a target for archery practice. Wrong target.
  3. False Dichotomy: Presenting an argument as if there are only two possible options when, in fact, there are more. Omitted options.
  4. Appeal to Ignorance: Arguing that a claim is true because it has not been proven false or vice versa. Faulty conclusions.
  5. Circular Reasoning/ begging the question: Using the conclusion of an argument as one of the premises, essentially restating the argument without providing new evidence. For example, “I am always right because I never make mistakes”. Faulty conclusions.
  6. Slippery Slope: Suggesting that a small action will lead to a chain of events resulting in a drastic and undesirable outcome without sufficient evidence. Faulty conclusions.
  7. Hasty Generalization: Drawing a general conclusion from insufficient or biased evidence. Faulty conclusions.
  8. Appeal to Authority: Relying on the opinion of an authority figure rather than presenting strong evidence or reasoning. Faulty conclusions.
  9. Post hoc Ergo Propter hoc (False Cause): Assuming that because one event follows another, the first event caused the second event. Faulty conclusions.
  10. Begging the Question: Assuming the truth of the conclusion in the premises of the argument. Faulty conclusions.
  11. Red Herring: Introducing an irrelevant topic to divert attention from the main issue being discussed. Added inapplicable data.
  12. Appeal to Emotion: Using emotional manipulation to persuade rather than presenting logical evidence. Added inapplicable emotion.
  13. Appeal to Tradition: Arguing that something is true or good solely because it has been done or believed for a long time. Added inapplicable data.
  14. Fallacy of Composition: Assuming that what is true for one part of something is true for the whole thing. Faulty conclusions.
  15. Fallacy of Division: Assuming that what is true for a whole is true for its individual parts. Faulty conclusions.
  16. Ownership Inversion Deflection: Taking the opponent’s argument as one’s own, then inverting it and arbitrarily restate things to deflect from the original. Fallacious inference. [first identified by J. E. Postma]

A relay of data on a situation can be analyzed using the above. To evaluate anything, one needs a standard of comparison, an ideal scene if you wish. To spot omitted data one has to have some familiarity or at least an idea of how the whole scene should look like. There is considerably more to proper data evaluation, but the above may already prove very useful.

In this book we will use all of the above to analyze and evaluate the philosophy and rationales of scientology.

[Extract from the soon to be published Vol. II Critique of Pure Scientology]