Identifying faulty reasoning
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Identifying Faulty Reasoning. A reasonable conclusion is based on data or evidence . Faulty reasoning occurs when the conclusion is not supported by the data.

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Identifying Faulty Reasoning

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Identifying faulty reasoning

Identifying Faulty Reasoning


Identifying faulty reasoning

A reasonable conclusion is based on data or evidence.

Faulty reasoning occurs when the conclusion is not supported by the data.


Identifying faulty reasoning

When you detect faulty reasoning, you need to obtain additional information to determine whether the conclusion is valid or not.


Overgeneralization

Overgeneralization

  • Overgeneralization – drawing a conclusion based on too little data.

    • A conclusion about a whole class of things is based on a few samples


Overgeneralization1

Overgeneralization

  • Example: “American Beauty rosebushes cannot survive long harsh winters, because in my garden two American Beauty rosebushes died over a long winter.” This is an overgeneralization based on two rosebushes.


Overgeneralization2

Overgeneralization

  • Example: “German shepherds shed their fur, and springer spaniels shed their fur. Therefore, all dogs shed fur.” This is an overgeneralization. Two types of dogs shed, but some kinds of dogs do not shed, such as poodles.


Illogical conclusion

Illogical Conclusion

  • Illogical conclusion – making an inference that is not supported by data.

    • Often indicate a cause-and-effect relationship that does not exist, based on coincidental events.


Illogical conclusion1

Illogical Conclusion

  • Example: Suppose you break a mirror and then fall on your way to school, losing your homework. You conclude that “Breaking mirrors causes bad luck.” This is an illogical conclusion based on two unrelated incidents.


Illogical conclusion2

Illogical Conclusion

  • Example: Suppose you hear on the television news that a mudslide followed a volcanic eruption. The news person said that mudslides are all caused by volcanic eruptions. This is an illogical conclusion based on one piece of evidence; other weather events can cause mudslides also.


Personal bias

Personal Bias

  • Personal Bias – basing conclusions on opinion rather than information.

    • Can lead to conclusions that are actually contradicted by the data.

    • Determine whether the author or speaker is trying to argue for a particular point of view.


Personal bias1

Personal Bias

  • Example: The speaker says, “The candidate is so stupid that he doesn’t know the capital of China.” The speaker gives no evidence and shows personal bias against the candidate.


Personal bias2

Personal Bias

  • Example: Your friends says, “I don’t like doing labs. Chemicals smell bad.” This is not a scientific statement; it us purely opinion.


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