p6-auto-connect-DE.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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Example 2: My political candidate gives 10% of his income to the needy, goes to church every Sunday, and volunteers one day a week at a homeless shelter.  Therefore, he is honest and morally straight.
Definition 3: Discarding the relevance of Premise 2 within the argument.
Example 3: My previous employer said I am a hard worker. My previous employer also said that I have the lowest score in customer relations. The score in customer relations is irrelevant to the new position in the sales team. Therefore, I am the perfect candidate for the sales team.

Task:
Carefully analyze the following fallacious argument:

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

Both Premise 1 and Premise 2 originate from a reputable scientific document.
The claim is deduced from information presented in Premise 1.
However, Premise 2 introduces doubt, suggesting that the claim is an invalid conclusion based on the scientific document.

Your objective is to precisely identify and articulate the fallacious reasoning in Premise 3 (the fallacious premise). This reasoning must robustly support the claim, ensuring that Premise 2 does not undermine the claim as a valid conclusion.
Do not repeat the claim itself, Premise 1, or Premise 2 when generating the fallacious Premise 3. Make sure the generated Premise 3 connects Premise 1 and Premise 2 to robustly support the claim, and ensure that Premise 2 does not undermine the claim as a valid conclusion.
Consider only fallacies from the provided fallacy inventory.
Present each fallacious premise alongside the applied fallacy class in this format:

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

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