Correlation Versus Causation

Correlation is defined in a dictionary as “a relationship that exists between two independent variables that are not absolute, but that has a probability of being positive.” In other words, in statistics, correlation or relationship is any statistically significant relationship, either causal or otherwise, between a set of bivariate or independent variables. In the widest sense, the correlation is simply the relationship that exists between a set of independent variables, although it more often than not refers to the extent to which a set of independent variables are linearly correlated.

Bivariate correlation, on the other hand, refers to an independent variable that is negatively correlated with another variable. By way of example, a person who smokes marijuana, for example, would have an increased likelihood of contracting lung cancer if he also smoked tobacco cigarettes.

Bivariate and causal correlation often go hand in hand and may be difficult to measure or disprove because there is usually some degree of interaction among the variables involved. Although the exact cause of a certain correlation cannot be established, it is generally accepted that causation is the result of two or more independent factors, such as, a person who eats more vegetables is also more likely to eat less fat.

Causal correlation has been proven to exist between the presence of one or more independent variables and health conditions, while the effects of these variables on another independent variable are not always congruent. For example, if a person is given two identical medications and given the chance to take one or the other, the majority of people tend to choose the one that they think is more effective.

Correlation can also be caused by the influence of another variable on the one you’re studying. For example, if a person is given a particular drug to treat his/her stomach pain, the person’s probability of having stomach pain will increase if the person also takes medication for his/her depression.

There are two main types of correlation that can be studied. The first is the direct relationship between the two variables. This means that the relationship between the variables is not the product of the products, but rather the product of the factors themselves. The second type of correlation is called a relationship that is indirect.

Indirect correlation occurs when a factor can cause another factor to increase in order to cause the other factor to decrease. For example, if the person taking the medication to treat his/her stomach pain, for example, takes a high-fat diet, his/her chance of having stomach pain will increase. However, if the same person who is taking a low-fat diet is also taking drugs to treat his/her depression, the probability of having stomach pain will decrease. This direct relationship can be explained when the stomach pain affects the depression because of the side effects of the high-fat diet, however, the depression causes the stomach pain to affect the depression as well.

In addition, there is also a third type of correlation that exists known as conditional correlation. This type of correlation refers to the relationship between variables that can only be determined through statistical methods (i.e., regression) while causal correlation is the only type of correlation that can be determined from observed results.

In the case of statistical correlation, most research on the relationship between health and nutrition can be concluded based on the fact that healthy people have healthier immune systems. While in the case of conditional correlation, researchers are able to determine that the presence of healthy food in a patient’s diet will lead to a healthier immune system.

Causal correlation and conditional correlations are the most common forms of correlation in medicine. This is why statistics plays such an important role in modern medicine.

It is also important to remember that correlation is not always the best way to make decisions about which medicine to use, and why. While correlation is extremely useful in determining why a treatment works, correlation cannot provide enough information about how to make a treatment work best. Instead, medical professionals must rely on their clinical training and experience in order to determine what the best course of action should be.