Analyze Statistics in the News
The correlation within a subject provides a prediction to how subjects connect. Although the correlation may show the likelihood of a reason and its impact on a relationship, yet it doesn't demonstrate causation.
Correlation indicates the possibility of a cause-effect relationship but it does not necessarily prove causation
On a basic level, all of these articles demonstrated that fundamentally if Subject 1 (S1) is bringing about Subject 2 (S2) to happen, or what is the same, that S2 is an outcome of S1. Regularly, when one peruses the news, simply understanding that so there is a relationship between the subject 1 and subject 2 however on a fundamental level, without any evidence that either one of them, even for the situation, will cause S2.
The correlation between two variables is one of the issues being referred to in statistics. Basically the inquiry would be the following: from an analytical approach the expectation is that the data from the variables will have a relationship between them. The one most often as possible considered is called straight relapse (by which we look for if there is no direct relationship between the variables).
From certain data from each of these variables one estimates if there is any relationship between them the one most frequently studied is called linear regression. The actual correlation between them is studied (ie, how strong is the relationship that we calculated based on the initial data) by a correlation coefficient.
There is also what could be considered a false correlation. Some studies reveal the relationship between two variables, but in essence the true revelation is the fact that there is no evidence of a true correlation.
The real question is when the false assumption implies causation? If the correlation does not support or imply that a relationship between two variables causes the other, but that does not means that if we find correlation between two variables can automatically rule one causes the other. There are cases in which S1 is the cause of occurrence S2, in others it is the reverse, in others there are some additional variable that makes this correlation occurs and sometimes it's all coincidence. The strong desire to believe that a relationship exist between to variables can misrepresent causation. The test is the ability to support the theory.
In the article “The Link Between Sleep and Weight” the correlation between sleep and weight are explored. "There are more and more studies showing that not getting enough sleep or not getting good quality of sleep can contribute to weight gain," says Raj Kakar, MD, MPH, the medical director at the Dallas Center for Sleep Disorders in Plano, Texas.
In my opinion the article is not convincing, to ensure causality between variables because there was insufficient statistical data supporting such causation. A strong argument supporting a causal link between lack of sleep and weight would include...