I Pity The Fool: Correlations, Predictions, and Causations
Naira Musallam, PhD • 27 Apr 2017
Nobody likes to be fooled. The majority of us want to know that they are being presented with accurate information about their topic of interest, be it market research, polling results, health related issues, financial trends, or anything else that is data related.
In the age of the data flood, understanding the differences between correlations, predictions, and causation are critical to making sound decisions and not being fooled by a fancy graph. Simply put, correlations mean association. It is mostly measured by a coefficient, Pearson (r), which tells you how much one variable tends to change when the other one does. Any ‘r’ can range between -1 to 1. When ‘r’ is positive, if one variable goes up the other one goes up as well. When ‘r’ is negative it means that when one variable goes up the other goes down. In short, it is about patterns in data.
Predictions on the other hand, while closely related to the concept of correlations in the sense of looking at relationships between two variables, are a bit different. They are about using the statistical techniques of regression models to come up with the best “fit line” of being able to determine one thing by the virtue of knowing the other.
What is critical to note here is just because one variable is correlated with another, or just because one variable is able to predict the other, it does not mean that it is causing it. This is due to the fact that one may not be able to determine things like plausible alternative explanations, time priority, or lack of control.
In order to be able to determine a cause-effect relationship, one needs to rely on randomized controlled experimental designs, where ideally you expose two or more groups to different conditions and you determine the effect of those conditions (variables/ interventions) on your dependent variable (outcome).
The importance of experimental designs will be discussed in a later post.
In the meantime, if you’ve ever wondered how the per capita consumption of mozzarella cheese correlates with civil engineering doctorates awarded, check out the link below. Hint: it’s always a good idea to dig deep into your data and truly understand what you’re looking at and the picture that it paints (read above!).
For even weirder relationships visit, Spurious Correlations.
Meet the author
Naira Musallam, PhD
Generative AI is Here to Push the Limits of Market Research
While the technology of generative AI has been around for quite some time, it wasn’t until the introduction of Lindsay • 30 Sep 2024
Consumer Spending Outlook: Summer 2024
Despite most economic indicators showing the US economy is in good shape by historical standards, consumer confidence has begun to dip once again, Savannah Trotter • 11 Jul 2024
How to Write Effective Concept Testing Survey Questions
Your concept tests are only as good as the questions you include. When done correctly, Savannah Trotter • 07 Jun 2024
Concept Validation Strategies and Methods
When you're ready to validate a new idea, Naira Musallam, PhD • 22 May 2024
Data Privacy & Generative AI in Market Research
In the digital age, data privacy and security are paramount, especially when utilizing powerful generative AI tools, like our Lindsay • 05 Apr 2024
Unleashing the Power of Survey Pages with Randomization and Looping
When designing a The SightX Research Team • 27 Mar 2024
Meet the author
Naira Musallam, PhD
Ready to meet the next generation of market research technology?
The Future of Market Research