PUB 550 Explain why correlation does not equal causation
PUB 550 Explain why correlation does not equal causation
“Correlation is not causation” means that just because two things correlate does not necessarily mean that one causes the other. Correlation means there is a relationship or pattern between the values of two variables. Causation means that one event causes another event to occur. Correlations between two things can be caused by a third factor that affects both of them. This is called the confounder. One of the most well-known and common examples of correlation but causation being in doubt is smoking and lung cancer. There might be a confounder that was responsible for the correlation between smoking and lung cancer. The increased rate could have been the result of better diagnosis, more industrial pollution, or more cars on the roads belching noxious fumes. And a study that was taken place in the UK in the 1950s which involved more than 40,000 doctors conclusively showed that smoking doesn’t really cause cancer. Correlation tests for a relationship between the two variables. The most common relationship is linear, meaning that any change in the explanatory variable will have a positive correlation with the dependent variable, in which case a simple regression model is often used to explore this relationship.
Corty, E. W. (2016). Using and interpreting statistics (3rd ed.). Worth Publishers.
Khan Academy. (n.d.). Correlation and causation | Lesson (article). Khan Academy. Retrieved July 11, 2022, from https://www.khanacademy.org/test-prep/praxis-math/praxis-math-lessons/gtp–praxis-math–lessons–statistics-and-probability/a/gtp–praxis-math–article–correlation-and-causation–lesson
There are two main reasons why correlation isn’t causation. These problems are important to identify for drawing sound scientific conclusions from research.
The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not. For example, ice cream sales and violent crime rates are closely correlated, but they are not causally linked with each other. Instead, hot temperatures, a third variable, affects both variables separately.
The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other. For example, vitamin D levels are correlated with depression, but it’s not clear whether low vitamin D causes depression, or whether depression causes reduced vitamin D intake.
You’ll need to use an appropriate research design to distinguish between correlational and causal relationships.
Correlational research is usually high in external validity, so you can generalize your findings to real life settings. But these studies are low in internal validity, which makes it difficult to causally connect changes in one variable to changes in the other.
These research designs are commonly used when it’s unethical, too costly, or too difficult to perform controlled experiments. They are also used to study relationships that aren’t expected to be causal.
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A correlation indicates a statistical relationship between two variables. We cannot infer that one variable changes the
other even if there is a link between the two. This association may be coincidental, or both variables might be changing as a result of a third component.
You may have heard the adage “correlation doesn’t indicate causation” when conducting research. The concepts of correlation and causation are interrelated. Since correlational studies frequently have high levels of external validity, you can extrapolate your results to actual environments. However, the low internal validity of this research makes it challenging to establish a link between changes in one variable and those in the other.
When doing controlled tests would be unethical, expensive, or complex, these study approaches are frequently utilized. Additionally, they are utilized to research associations that aren’t always casual.
Without controlled studies, it might be difficult to determine if changes in one variable were brought on by another. Any additional variable that can influence your results and is not one of your key factors is referred to as an extraneous variable.
In correlational research, limited control indicates that additional or confounding factors serve as alternative explanations for the findings. When they are not confounding, variables might give the impression that a correlational link is causative.
Scribbr, (2021). Correlation vs. causation l Differences, designs & examples. Retrieved from: https://www.scribbr.com/methodology/correlation-vs-causation/
Correlation reminds me of the word “relation”. Two variables can have a relation with another without being a causation. Sometimes it may be causation but we cannot assume causation. For example, with breast cancer, there can be so many factors as to why breast cancer had to exist in the body. It can be unhealthy eating, genetic health patterns, trauma experiences, and exposures to various chemicals. We cannot know why breast cancer exists but all we can do is draw associations instead of experiments because we cannot give all individuals cancer as an experiment because it would break the oath of “do no harm”. Dr. Saul McLeod (2020) mentions that correlation allows the researcher to investigate variables that are natural interactions. For cancer patients, all we can do is look for contributing factors.
Dr. Saul Mcleod (2020). Correlation Definitions, Interpretions and Examples.
Correlation and causation are significant because they allow policies and programs that aim to bring about the desired outcome to be better targeted. Correlation and causation enable researchers to explain the current public health concerns, predict future outcomes, and create interventions targetting the cause of change in the outcome. Researchers use correlation and causation to investigate the relationships and put everything together to draw general conclusions. If two factors are not correlated in research, researchers look at causation to expand their knowledge to study details that can lead to new findings and results.
Statistical language – correlation and causation. Australian Bureau of Statistics. (n.d.). Retrieved July 11, 2022, from https://www.abs.gov.au/websitedbs/D3310114.nsf/home/statistical+language++correlation+and+causation#:~:text=A%20correlation%20between%20variables%2C%20however,relationship%20between%20the%20two%20events.
Correlation conducts test to check the relationship between two variables. Two variables may be related but it does not necessarily mean the one variable causes the other variable to occur (JMP. Statistical Discovery, n.d.). There are also instances that strong correlation may indicate the cause, but it is still vital to check for other possibilities such as random chances, where variables appear to be related but there is no relationship involved. Another possible explanation, when there is third variable which may influence the relationship to look strong or weak (JMP. Statistical Discovery, n.d.)
Correlation between variables shows when there is a pattern among the data collected. It may show that variables are moving together but correlation that not suffice if the variables are moving together.
JMP. Statistical Discovery (n.d.). Correlation vs Causation. Retrieved from https://www.jmp.com/en_us/statistics-knowledge-portal/what-is-correlation/correlation-vs-causation.html#:~:text=Correlation%20tests%20for%20a%20relationship,correlation%20does%20not%20imply%20causation.%E2%80%9D
Correlation is a statistical measure that describes the degree of relationship between two variables by using a single value that range from -1 to +1(Boston university of Public Health, 2016). To describe correlation, a unit free measure called a correlation coefficient that ranges from -1 to +1 and is denoted by the letter “r” is used correlation means association because it measures the extent of to which two variables are related (McLeod, 2020). Mcleod (2020), further identified that a correlational study can either be positive, negative or have no correlation.
According to Boston University of Public Health (2016), “Negative values of correlation indicate that as one variable increases the other variable decreases”, and “Positive values of correlation indicates that as one variable increase the other variable increases as well. A zero correlation exist when there is no relationship between two variables. Correlations are used for prediction, validation, and reliability (McLeod, 2020).
Correlation does not mean causation because a change in one variable does not automatically means that the change is caused by the change in the value of the other variable (McLeod, 2020). For example, McLeod (2020) identified that being a patient in a hospital is correlated with dying, but this does not mean that one event (being in a hospital) causes the other (dying), because another third variable such as diet or level of exercise may be involved and be the cause of dying.
Boston University of Public Health. (2016). Correlation
McLeod, S. (2020). Correlation Definitions, Examples & Interpretation