Faulty Cause and Effect
(See also: Fallacy: Confusing Cause and Effect)
This technique suggests that because B follows A, A must cause B.
Remember, just because two events or two sets of data are related does not necessarily mean that one caused the other to happen. It is important to evaluate data carefully before jumping to a wrong conclusion.
In order to determine that a fallacy has been committed, it must be shown that the causal conclusion has not been adequately supported and that the person committing the fallacy has confused the actual cause with the effect. Showing that the fallacy has been committed will typically involve determining the actual cause and the actual effect.
False Cause:
A temporal order of events is confused with causality; or, someone oversimplifies a complex causal network. The false cause fallacy assumes that because event B follows event A, Event B was caused by event A. Relationships are not cause and effect just because they occur at the same time. Another name is the improper data fallacy. A generalization that attributes something to a false cause is based on faulty data.
Example:
Madeleine immediately declared "abducted".
- While "checking on the children", Kate "discovered" Maddie gone and her immediate response was "They've taken her".
- When Gerry was asked by the neighbor Mrs. Fenn what was going on, he said "A child's been abducted" rather than "My daughter is missing."
The truth may have been:
- Maddie could have woken up and wandered, looking for her parents.
- Maddie could have been hiding in the apartment.
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