PUB 550 Explain why correlation does not equal causation
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.
Reference
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
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.
Reference:
Scribbr, (2021). Correlation vs. causation l Differences, designs & examples. Retrieved from: https://www.scribbr.com/methodology/correlation-vs-causation/
Click here to ORDER an A++ paper from our Verified MASTERS and DOCTORATE WRITERS PUB 550 Explain why correlation does not equal causation:
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.
References
Boston University of Public Health. (2016). Correlation
McLeod, S. (2020). Correlation Definitions, Examples & Interpretation
Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It’s a common tool for describing simple relationships without making a statement about cause and effect.
Scatterplots are also useful for determining whether there is anything in our data that might disrupt an accurate correlation, such as unusual patterns like a curvilinear relationship or an extreme outlier.
Correlations can’t accurately capture curvilinear relationships. In a curvilinear relationship, variables are correlated in a given direction until a certain point, where the relationship changes.
The correlation coefficient, which has a range of -1 to +1, is used to describe correlations. Its symbol is r. A p-value serves as an indicator of statistical significance. As a result, correlations are frequently expressed as two essential numbers: r= and p=.
The linear connection is weaker the closer r comes to zero.
When both variables’ values tend to rise in tandem, there is a positive correlation, as indicated by positive r values.
A negative r value is present when there is a negative correlation, where values of one variable tend to increase as values of the other variable decrease.
The p-value provides proof that, based on what we see in the sample, we may infer meaningfully that the population correlation coefficient is most likely not zero.
In our example, the value supplied for r is not on the same scale as either height or temperature, which is known as a “unit-free measure,” which indicates that correlations exist on their own scale. Compared to other summary statistics, this is unique. The average elevation measurement, for instance, is on the same scale as its variable.
Reference:
Scribbr, (2021). Correlation vs. causation l Differences, designs & examples. Retrieved from: https://www.scribbr.com/methodology/correlation-vs-causation/
Correlation is a statistical measure that describes the size and direction of a relationship between two or more variables (SITN, 2021). So basically, correlation is an action or occurrence that can cause another. An example of correlation is smoking causes an increase in the risk of developing lung cancer, or it can correlate with another but does not necessarily mean that the two variables cause one another. Another example is that smoking is associated with alcoholism but does not cause alcoholism (SITN, 2021). A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. Causation indicates that one event results from the occurrence of the other event (SITN, 2021). There is a causal relationship between the two events, called cause and effect. In practice, however, it does not remain easy to establish cause and effect, compared with showing correlation. Correlation does not equal causation because to point out that correlation between two variables does not necessarily mean that one variable causes the other to occur. To add, the variables can be 95% correlated but that missing 5% is all that is needed to show that the variables move away from each other. When there is an inability to develop a cause and effect relationship between two events or variables, correlation does not equal causation.
Reference:
Harward University, 27 Jan. 2021, “When Correlation Does Not Imply Causation: Why Your Gut Microbes May Not (Yet) Be a Silver Bullet to All Your Problems.” Science in the News, sitn.hms.harvard.edu/flash/2021/when-correlation-does-not-imply-causation-why-your-gut-microbes-may-not-yet-be-a-silver-bullet-to-all-your-problems/.
Correlation does not equal causation because it does not necessarily mean that one is caused by the other. There can always be another factor in the equation. For example, when there are two variables that are correlated, it has no meaning on one variable causing the other. Correlation cannot mean causation. Correlation does not show the cause or effect of the relationship between the two things or variables. The correlation will only show the link or the association between variables but not the cause of one variable to the other or how one variable causes changes in the other variable. Paul (2022) mentions that the concept of “causation” operate to make accuracy of research difficult. Causation can cause researchers to vere off the goal at hand instead of looking at all of the various factors.
There are positive and negative correlations. For example, a negative correlation is when one variable causes the downfall of another. High cholesterol is highly associated with high blood pressure and heart disease. Research shows that both are correlated to one another but there is no evidence that high cholesterol causes heart disease directly even though it could contribute to it because every person is different and have different body types and there are so many other human factors that are at play. For positive correlation, increase in height means greater weight. Because more weight is needed to sustain height.
References:
Paul, S. (2022). Proving Causation in Clinical Research Negligence. Virginia Law Review, 108(2), 535–579.