The Skeptic's Field Guide: False Cause; Correlation Error
The 'False Cause' fallacy attributes cause and effect relationship when none is proven to exist. You presumed that a real or perceived relationship between things means that one is the cause of the other. In the terms of propaganda 'faulty cause and effect' or the 'Cause-and-effect fallacy' is a frequently used method for the tactic known as Self-Evidence.
Aaron Fibreglass is writing up his report on the link between self-esteem and obesity. We need to raise the self-esteem of obese people to help them overcome their weight problem. However, if Beijing is undergoing economic growth and its citizens happen to practice Feng Shui, it does not follow that Feng Shui is the cause of the economic growth. This relationship may simply be apparent rather than real — that is, a coincidence.
To establish whether or not Feng Shui can influence economic prosperity, systematic tests would need to be conducted.
In fact at any one time, a great many cities around the world are going through economic growth. Few, if any city administrators give any consideration to Feng Shui. There are no doubt a great many other cites in China where Feng Shui is practiced.
What is their economic activity like? The seeker after truth should always ask questions which go beyond mere association, and look for alternative possibilities. In the second example, Aaron claims low self-esteem causes obesity.
- Confusing Cause and Effect Examples
- False Effect
However on the evidence presented, causation could be in the opposite direction — obesity could be the cause of low self-esteem. Or both could be caused by a third, unidentified variable.
To a skeptical scientist, such a strong correlation between obesity and low self-esteem is potentially of great interest, but a series of sophisticated follow-up studies would be needed to determine the nature of the correlation and the direction of causation.
False cause can have very serious consequences. For example, the false cause fallacy during the European dark ages led to the widespread belief that illness, famine and personal misfortune was caused by black magic and sorcery. Such beliefs led to "witch-hunts" literally and unfounded but widely believed accusations of sorcery.
Correlation does not imply causation
Thus there can be no conclusion made regarding the existence or the direction of a cause-and-effect relationship only from the fact that A and B are correlated. Determining whether there is an actual cause-and-effect relationship requires further investigation, even when the relationship between A and B is statistically significanta large effect size is observed, or a large part of the variance is explained.
Examples of illogically inferring causation from correlation[ edit ] B causes A reverse causation or reverse causality [ edit ] Reverse causation or reverse causality or wrong direction is an informal fallacy of questionable cause where cause and effect are reversed.
The cause is said to be the effect and vice versa. Example 1 The faster windmills are observed to rotate, the more wind is observed to be. Therefore wind is caused by the rotation of windmills. In this example, the correlation simultaneity between windmill activity and wind velocity does not imply that wind is caused by windmills. Wind can be observed in places where there are no windmills or non-rotating windmills—and there are good reasons to believe that wind existed before the invention of windmills.
Therefore, high debt causes slow growth.
Your logical fallacy is false cause
This argument by Carmen Reinhart and Kenneth Rogoff was refuted by Paul Krugman on the basis that they got the causality backwards: Children that watch a lot of TV are the most violent.
Clearly, TV makes children more violent. This could easily be the other way round; that is, violent children like watching more TV than less violent ones.
Example 4 A correlation between recreational drug use and psychiatric disorders might be either way around: Gateway drug theory may argue that marijuana usage leads to usage of harder drugs, but hard drug usage may lead to marijuana usage see also confusion of the inverse.
Correlation does not imply causation - Wikipedia
Indeed, in the social sciences where controlled experiments often cannot be used to discern the direction of causation, this fallacy can fuel long-standing scientific arguments. Example 5 A historical example of this is that Europeans in the Middle Ages believed that lice were beneficial to your health, since there would rarely be any lice on sick people. The reasoning was that the people got sick because the lice left. The real reason however is that lice are extremely sensitive to body temperature.
A small increase of body temperature, such as in a feverwill make the lice look for another host. The medical thermometer had not yet been invented, so this increase in temperature was rarely noticed.
Noticeable symptoms came later, giving the impression that the lice left before the person got sick. One making an argument based on these two phenomena must however be careful to avoid the fallacy of circular cause and consequence.
Poverty is a cause of lack of education, but it is not the sole cause, and vice versa. Third factor C the common-causal variable causes both A and B[ edit ] Main article: Spurious relationship The third-cause fallacy also known as ignoring a common cause  or questionable cause  is a logical fallacy where a spurious relationship is confused for causation. It is a variation on the post hoc ergo propter hoc fallacy and a member of the questionable cause group of fallacies.
All of these examples deal with a lurking variablewhich is simply a hidden third variable that affects both causes of the correlation.
Example 1 Sleeping with one's shoes on is strongly correlated with waking up with a headache. Therefore, sleeping with one's shoes on causes headache.
The above example commits the correlation-implies-causation fallacy, as it prematurely concludes that sleeping with one's shoes on causes headache. A more plausible explanation is that both are caused by a third factor, in this case going to bed drunkwhich thereby gives rise to a correlation. So the conclusion is false. Example 2 Young children who sleep with the light on are much more likely to develop myopia in later life. Therefore, sleeping with the light on causes myopia.