#Causality vs correlation how to

In this instance, the correlation between ice cream and homicide are mere statistical coincidences. So do we blame the harmless ice cream for increased crime rates? The example shows when two or more variables correlate, people are tempted to conclude a relationship between them.

This theory attempts to establish a correlation between increasing sales of ice creams with the rate of homicides. Take, for instance, the ice cream-homicide fallacy. It is pretty common to find reliable correlations for two variables, only to discover that they are not at all causally linked. Correlation between variables shows a pattern in the data and that these variables tend to вЂmove togetherвЂ™. One of the basic tenets of statistics is: correlation is not causation. Shall we credit storks for airlifting the babies?вЂќ Neil deGrasse Tyson, American astrophysicist. In the 1990s, the stork population in Germany increased and the German at-home birth rates rose as well. So, have an organic apple in peace and go do some science.вЂњBut to measure cause and effect, you must ensure that simple correlation, however tempting it may be, is not mistaken for a cause.

With careful documentation, intuitive thinking, and scrupulous study, together we are able to discover the secrets of the universe and to separate out these curveballs it likes to throws at us. Well-designed experimental studies, scientific models, statistical methods, and the scientific method in general is the scientistвЂ™s best friend. Here, the cause and effect mechanism must comply with the known laws of nature вЂ“ this (at least) gives us somewhere to start. In other words, there must be some real mechanism connecting the two events (assuming the correlation isnвЂ™t completely coincidental). If a correlation is established, how can you determine causation? Are they caused by the same thing? Does one cause the other? Are they completely unrelated? In order for one event to cause another, they must be related. The list of both real-world scientific examples and make up logical exercises goes on, but that gives you the general idea. Instead, OSU demonstrated a strong link between parental myopia and the development of child myopia, the study also noted that nearsighted parents where more likely to leave their childвЂ™s light on at night. Later, thanks to the peer review process, the Ohio State University disproved this claim and showed that myopia wasnвЂ™t caused by sleeping with the light on. This example comes from a study done by the University of PennsylvaniaвЂ™s Medical Center in 1999. Therefore, sleeping with the light on causes myopia. Young children who sleep with the light on are much more likely to develop myopia later in life ( myopia is near-sightedness).

#Causality vs correlation how to

As the exposure to water-based activities increases, the number of people who donвЂ™t know how to swim or who simply get in trouble while swimming also increases which results in a higher drowning rate. When itвЂ™s hot outside, youвЂ™re more likely to eat ice cream and youвЂ™re more likely to swim. In this case, the sale of ice cream and the rate of drowning is caused by a common cause вЂ“ the summer months. It completely fails to account for the time/season and the temperature. Obviously, the above example is ludicrous.

As ice cream sales increase, the rate of drowning deaths increases.

Correlation is not causation, but it sure is a hint.вЂќ Here are some further examples demonstrating this logical fallacy: He suggests that causality and correlation can be best summarized by saying вЂњEmpirically observed covariation (correlation) is necessary but not a sufficient condition for causality. Edward Tufte criticizes the statement вЂњcorrelation does not imply causationвЂќ by suggesting itвЂ™s inaccurate and incomplete. When two events are correlated, further study is needed to prove whether or not they are related. Because causation proves correlation, you canвЂ™t have two unrelated events that affect each other (in other words, they must be correlated). As stated above, there are at least five distinct ways the data can be explained. The argument immediately leaps from a correlation to a conclusion rather prematurely.