p1-basic-DLE.txt

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Fallacy Inventory:

Ambiguity:
Definition 1: When an unclear phrase with multiple definitions is used within the argument; therefore, does not support the conclusion.
Logical Form 1: Claim X is made. Y is concluded based on an ambiguous understanding of X.
Example 1: It is said that we have a good understanding of our universe.  Therefore, we know exactly how it began and exactly when.
Definition 2: When the same word (here used also for phrase) is used with two different meanings.
Logical Form 2: Term X is used to mean Y in the premise. Term X is used to mean Z in the conclusion.
Example 2: A feather is light. What is light cannot be dark. Therefore, a feather cannot be dark.

Impossible Expectations:
Definition 1: Comparing a realistic solution with an idealized one, and discounting or even dismissing the realistic solution as a result of comparing to a “perfect world” or impossible standard, ignoring the fact that improvements are often good enough reason.
Logical Form 1: X is what we have. Y is the perfect situation. Therefore, X is not good enough.
Example 1: Seat belts are a bad idea. People are still going to die in car crashes.

False Equivalence:
Definition 1: Assumes that two subjects that share a single trait are equivalent.
Logical Form 1: X and Y both share characteristic A. Therefore, X and Y are [behave] equal.
Example 1: They are both Felidae, mammals in the order Carnivora, therefore there's little difference between having a pet cat and a pet jaguar.

False Dilemma:
Definition 1: Presents only two alternatives, while there may be another alternative, another way of framing the situation, or both options may be simultaneously viable.
Logical Form 1: Either X or Y is true.
Example 1: I thought you were a good person, but you weren’t at church today.
Definition 2: Making the false assumption that when presented with an either/or possibility, that if one of the options is true that the other one must be false.
Logical Form 2: P or Q could be true. P is true. Therefore, Q is not true.
Example 2: Bill is 6’11” tall, thin, but muscular.  We know he either is a pro basketball player or a jockey.  We conclude that it is more probable that he is a pro basketball player than a pro basketball player or a jockey.

Biased Sample Fallacy:
Definition 1: Drawing a conclusion about a population based on a sample that is biased, or chosen in order to make it appear the population on average is different than it actually is.
Logical Form 1: Sample S, which is biased, is taken from population P. Conclusion C is drawn about population P based on S.
Example 1: Based on a survey of 1000 American homeowners, 99% of those surveyed have two or more automobiles worth on average $100,000 each.  Therefore, Americans are very wealthy.

Hasty Generalization:
Definition 1: Drawing a conclusion based on a small sample size, rather than looking at statistics that are much more in line with the typical or average situation.
Logical Form 1: Sample S is taken from population P. Sample S is a very small part of population P. Conclusion C is drawn from sample S and applied to population P.
Example 1: My father smoked four packs of cigarettes a day since age fourteen and lived until age sixty-nine.  Therefore, smoking really can’t be that bad for you.

Causal Oversimplification:
Definition 1: Post hoc ergo propter hoc - after this therefore because of this. Automatically attributes causality to a sequence or conjunction of events.
Logical Form 1: A is regularly associated with B; therefore, A causes B.
Example 1: Every time I go to sleep, the sun goes down.  Therefore, my going to sleep causes the sun to set.
Definition 2: Assumes there is a single, simple cause of an outcome.
Logical Form 2: X is a contributing factor to Y. X and Y are present. Therefore, to remove Y, remove X.
Example 2: Smoking has been empirically proven to cause lung cancer. Therefore, if we eradicate smoking, we will eradicate lung cancer.

Fallacy of Composition:
Definition 1: Inferring that something is true of the whole from the fact that it is true of some part of the whole.
Logical Form 1: A is part of B. A has property X. Therefore, B has property X.
Example 1: Hydrogen is not wet.  Oxygen is not wet.  Therefore, water (H2O) is not wet.
Definition 2: Inferring that something is true of one or more of the parts from the fact that it is true of the whole.
Logical Form 2:  A is part of B. B has property X. Therefore, A has property X.
Example 2:  His house is about half the size of most houses in the neighborhood. Therefore, his doors must all be about 3 1/2 feet high.

Fallacy of Exclusion:
Definition 1: When only select evidence is presented in order to persuade the audience to accept a position, and evidence that would go against the position is withheld.
Definition 2: Ignores relevant and significant evidence when inferring to a conclusion.
Logical Form 2: Evidence A and evidence B is available. Evidence A supports the claim of person 1. Evidence B supports the counterclaim of person 2. Therefore, person 1 presents only evidence A.
Example 2: Hydrogen is not wet.  Oxygen is not wet.  Therefore, water (H2O) is not wet.
Definition 3: Discarding the relevance of Premise 2 within the argument.
Logical Form 3: Evidence A and evidence B is available. Evidence A supports the claim of person 1. Evidence B supports the counterclaim of person 2. Therefore, evidence B is irrelevant to the claim.
Example 3:  His house is about half the size of most houses in the neighborhood. Therefore, his doors must all be about 3 1/2 feet high.

Task:
Examine the following fallacious argument:

Premise 1: "@@p0@@"
Premise 2: "@@context@@"
Premise 3: ""
Therefore: "@@claim@@"


Premises 1 and 2 are sourced from the same credible scientific document.
The claim is based on the information in Premise 1.
However, Premise 2 suggests that the claim is an invalid conclusion from the scientific document.

Your task is to identify and verbalize the fallacious reasoning in Premise 3 (the fallacious premise) that is necessary to support the claim, despite the conflicting information in Premise 2.
Only consider fallacies from the provided fallacy inventory.

Present each fallacious premise along with the applied fallacy class in this format:

    Fallacious Premise: <fallacious premise>; Applied Fallacy Class: <applied fallacy class>.

If there are multiple applicable fallacies, list them in order of relevance.