B causes A (reverse causation or reverse causality) 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 that windmills are observed to rotate, the more wind is observed. :Therefore, wind is caused by the rotation of windmills. (Or, simply put: windmills, as their name indicates, are machines used to produce wind.) In this example, the correlation (simultaneity) between windmill activity and wind velocity does not imply that wind is caused by windmills. It is rather the other way around, as suggested by the fact that wind does not need windmills to exist, while windmills need wind to rotate. 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. ;Example 2 :Low cholesterol is associated with an increase in mortality. :Therefore, low cholesterol increases your risk of mortality. Causality is actually the other way around, since some diseases, such as cancer, cause low cholesterol due to a myriad of factors, such as weight loss, and they also cause an increase in mortality. This can also be seen in alcoholics. As alcoholics become diagnosed with cirrhosis of the liver, many quit drinking. However, they also experience an increased risk of mortality. In these instances, it is the diseases that cause an increased risk of mortality, but the increased mortality is attributed to the beneficial effects that follow the diagnosis, making healthy changes look unhealthy.
Example 3 In other cases it may simply be unclear which is the cause and which is the effect. For example: :
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: perhaps the drugs cause the disorders, or perhaps people use drugs to
self medicate for preexisting conditions.
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). 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. One such example can be found in
education economics, between the
screening/
signaling and
human capital models: it could either be that having innate ability enables one to complete an education, or that completing an education builds one's ability.
Example 5 A historical example of this is that Europeans in the
Middle Ages believed that
lice were beneficial to 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
fever, makes the lice look for another host. The medical
thermometer had not yet been invented and so that increase in temperature was rarely noticed. Noticeable symptoms came later, which gave the impression that the lice had left before the person became sick. In other cases, two phenomena can each be a partial cause of the other; consider poverty and lack of education, or procrastination and poor self-esteem. 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 The
third-cause fallacy (also known as
ignoring a common cause or
questionable cause the study received much coverage at the time in the popular press. However, a later study at
Ohio State University did not find that
infants sleeping with the light on caused the development of myopia. It did find a strong link between parental myopia and the development of child myopia, also noting that myopic parents were more likely to leave a light on in their children's bedroom. In this case, the cause of both conditions is parental myopia, and the above-stated conclusion is false. ;Example 3 :As ice cream sales increase, the rate of drowning deaths increases sharply. :Therefore, ice cream consumption causes drowning. This example fails to recognize the importance of time of year and temperature to ice cream sales. Ice cream is sold during the hot summer months at a much greater rate than during colder times, and it is during these hot summer months that people are more likely to engage in activities involving water, such as
swimming. The increased drowning deaths are simply caused by more exposure to water-based activities, not ice cream. The stated conclusion is false. ;Example 4 :A hypothetical study shows a relationship between test anxiety scores and shyness scores, with a statistical
r value (strength of correlation) of +.59. :Therefore, it may be simply concluded that shyness, in some part, causally influences test anxiety. However, as encountered in many psychological studies, another variable, a "self-consciousness score", is discovered that has a sharper correlation (+.73) with shyness. This suggests a possible "third variable" problem, however, when three such closely related measures are found, it further suggests that each may have bidirectional tendencies (see "
bidirectional variable", above), being a cluster of correlated values each influencing one another to some extent. Therefore, the simple conclusion above may be false. ;Example 5 :Since the 1950s, both the atmospheric
CO2 level and
obesity levels have increased sharply. :Hence, atmospheric CO2 causes obesity. Richer populations tend to eat more food and produce more CO2. ;Example 6 :
HDL ("good")
cholesterol is negatively correlated with incidence of heart attack. :Therefore, taking medication to raise HDL decreases the chance of having a heart attack. Further research has called this conclusion into question. Instead, it may be that other underlying factors, like genes, diet and exercise, affect both HDL levels and the likelihood of having a heart attack; it is possible that medicines may affect the directly measurable factor, HDL levels, without affecting the chance of heart attack.
Bidirectional causation: A causes B, and B causes A Causality is not necessarily one-way; in a
predator-prey relationship, predator numbers affect prey numbers, but prey numbers, i.e. food supply, also affect predator numbers. Another well-known example is that cyclists have a lower
Body Mass Index than people who do not cycle. This is often explained by assuming that cycling increases
physical activity levels and therefore decreases BMI. Because results from prospective studies on people who increase their bicycle use show a smaller effect on BMI than cross-sectional studies, there may be some reverse causality as well. For example, people with a lower BMI may be more likely to want to cycle in the first place.
The relationship between A and B is coincidental The two variables are not related at all, but correlate by chance. The more things are examined, the more likely it is that two unrelated variables will appear to be related. For example: • The result of the last home game by the
Washington Commanders prior to the presidential election
predicted the outcome of every presidential election from 1936 to 2000 inclusive, despite the fact that the outcomes of football games had nothing to do with the outcome of the popular election. This streak was finally broken in
2004 (or
2012 using an alternative formulation of the original rule). • The
Mierscheid law, which correlates the
Social Democratic Party of Germany's share of the
popular vote with the size of crude steel production in Western Germany. • Alternating
bald–hairy Russian leaders: A bald (or obviously balding) state leader of Russia has succeeded a non-bald ("hairy") one, and vice versa, for nearly 200 years. • The
Bible code, Hebrew words predicting historical events supposedly hidden within the
Torah: the huge number of combinations of letters makes appearances of any word in sufficiently lengthy text statistically insignificant. ==Use of correlation as scientific evidence==