The most common method to control confounding variables is with controlled studies. A confounding variable can be what the actual cause of a correlation is, hence any studies must take these into account and find ways of dealing with them, usually by searching them out and trying to alter this variable directly. In this case, age influences both reading ability and shoe size quite directly. The part age plays in this example is known as a ' confounding variable' or 'confounding factor', and is something that is not being controlled for in the experiment. In this case, the two variables are more accurately correlated with a third: age. Instead it's caused by the fact that young children have small feet and have not yet (or only recently) been taught to read. If someone performed such a survey, s/he would find that the larger shoe sizes correlate with better reading ability, but this does not mean large shoes cause good reading skills. An example is the relationship between reading ability and shoe size across the whole population of the United States. Two events can consistently correlate with each other but not have any causal relationship. The Moon is just far enough away to be the same size as the Sun in the sky because it had to be somewhere.) It is simply coincidence or a statistical fluke (rare, but it does happen.This willingness explains most of the rise in autism rates.) (Both vaccination rates and the willingness of doctors to diagnose autism have risen over the years. However, is highly correlated with - making it appear as though is the cause.

(a confounding variable) is the true cause of.

A confounding variable is the cause: is correlated with.

Let's assume that autism rates are correlated with vaccination rates: (Often, several identical experiments are run with different levels of the experimental variable, such as temperature or drug dosage.) If there's still a correlation, then causation is very, very likely.ДЇor any given correlation, there are four basic possibilities. In a controlled scientific experiment, causation can be teased out of a correlation by removing all possible confounding variables. However: It is not true that correlation cannot imply causation. The assumption of causation is false when the only evidence available is simple correlation. The reality is that cause and effect can be indirect due to a third factor known as a confounding variable - or that causality can be the reverse of what is assumed. For example: Both vaccination rates and autism rates are rising (perhaps even correlated), but that does not mean that vaccines cause autism anymore than it means that autism causes vaccines. The form of fallacy that it addresses is known as post hoc, ergo propter hoc.

вЂњ вЂќI used to think I could control ducks with my mind but it turns out ducks & I just have very similar ideas about what stuff ducks should do.Д¬orrelation does not imply causation is the logically valid idea that events which coincide with each other are not necessarily caused by each other.

Causality does not imply correlation