The term “correlation” was first used by an English statistician (named Richard Hodgson), in 1814. He used it in the context of medical research and described how, for example, a patient who is suffering from asthma, heart disease and obesity may have the same symptoms at any given time. However, it has since been used in a broader context to refer to statistical relationships between different sets of variables. Some of the most common examples of correlation are as follows: between a patient’s height and weight, age of the person, between a patient’s allergies and symptoms, among many others. The more variables the person has to choose from the better.
The most significant relationships are most often found among those variables that are very similar – there should be no big difference between the variables, or they will not be able to create a meaningful relationship. The more statistically insignificant the relationship between the variables, the weaker it is, and the less predictive it is for future outcomes.
Correlation does not always work in favor of one group over another. For example, if two of your friends are smokers and you asked them if they had recently changed their minds, you might find that they both answered no. This may indicate that smokers do not believe that they are addicted to nicotine – the correlation between smoking and quitting could simply be random, unrelated to either. However, if these same two friends both smoke daily, and are still together, this would also suggest that they are not currently addicted.
There is a correlation between Parkinson’s disease and weight, but this is far from random. If someone is overweight, they might be suffering from this condition. However, if they are overweight, they are also more likely to also be suffering from Parkinson’s. Similarly, if a patient is suffering from heart disease, they are more likely to also have been diagnosed with Parkinson’s if they have the same type of medical problem.
The same applies for certain diseases, such as cancer. A correlation between cholesterol levels and being diagnosed with cancer is extremely important. However, it is not a strong enough reason to conclude that the patient is already afflicted with the disease. The correlation between cigarette smoking and heart disease is also not very strong, but it is not necessarily negative either. It means that smokers tend to have a higher risk of having these two conditions, but it does not mean they cannot also lead healthy lives.
Statistical analysis can help you identify certain patterns, such as the fact that people with high cholesterol tend to have high risks of developing high blood pressure. If you want to make sure that you are healthy, then the first thing you need to do is to keep an eye on your cholesterol levels, and get them checked regularly. The second thing you need to do is to reduce your chances of developing cancer. It is important that you look at your overall health – take a look at your diet, exercise, and quit smoking. This is just as much a way of reducing your risk of cancer, or in the case of smoking, lowering it, as it is a way of avoiding it.
Correlation is something that can be seen, felt and measured. In order to understand it, and apply it, however, you need to look at it under the microscope.