The regression fallacy attributes a natural return to average to a cause that happened in between. When an extreme situation moves back toward normal, an intervening event is treated as the cause.
Example
“After the ritual, the illness improved. The ritual worked.”
(It may be natural variation or expected course.)
Applied example (political)
“After the announcement, unemployment returned to normal; it is attributed to the announcement.” (It may be natural regression.)
Applied example (mystical)
“After the prayer, the pain decreased as it usually does; it is attributed to the prayer.” (Variation is mistaken for cause.)
Why it is fallacious
- It ignores regression toward the mean.
- It confuses temporal coincidence with causality.
- It overinterprets intervening events.
How to spot it
- A normal change is credited to a recent action.
- No control group is considered.
- The claim starts from extreme cases.
How to respond
- Ask for comparative evidence and controls.
- Consider the natural evolution of the phenomenon.
- Evaluate causality with data, not coincidence.