Have you ever seen a Pinterest graph that a displays somewhat obscure graph that shows a correlation between two unrelated subject, and thought to yourself “I wonder how these could be related. Do they cause each other, or is this just Pinterest being Pinterest. Ok, maybe that was too specific. But you get the idea. The idea of correlation versus causation was what fuelled our most recent Scimatics project.
This project has been focused on the scientific subject of Correlation and Causation. Correlation is a statistical technique used to show the relationship between two or more variables, while Causation indicates that one event is the result of the occurrence of the other event. The classic causation vs correlation example that is frequently used is that smoking is correlated with alcoholism, but doesn’t cause alcoholism, while smoking causes an increase in the risk of developing lung cancer. One may think that they are able to cruise through life not knowing the difference between the correlation and causation. Incorrect. Get that tomfoolery of a mindset out of your head right now. Ninth grade science is coming to the rescue.
Both correlation and causation are used to scientifically support graphs such as this one.
As we see here, the increase of butter sales mimics the divorce rate in the United States. We can interpret this graph in several different ways. 1. Margarine is more often consumed amongst happy couples. Perhaps this indicates that happier couples eat more margarine. There is an idea for a commercial. 2. Happy marriages cause more margarine sales. Because there is nothing like margarine to drown out your sorrow. 3. The two decreases are unrelated. How are we able to distinguish between these three options? This is where our project comes into play.
At the beginning of this project, we created a mind map using the application MindNode. Young, and unenlightened Ally did not know much about this subject, so I laid out all of the questions that I had, and compiled them into what I wanted to learn throughout the course of this project.
After we completed this project, I re-annotated my MindNode. I included answers to my questions, as well as steps in the project which taught me these answers.
Meg, who was my partner for this project, and I conducted several surveys with the intention of creating a correlation graph of our own. This was the final outcome that we hoped for. Along the way we surveyed, wrote, Kahooted and graphed our way to a final outcome that I was proud of.
Applying and Innovating
Contribute to care for self, others, community, and our world through individual or collaborative approaches
When we first began this project, I did not know how to conduct a survey, nonetheless an unbiased one. The very first question that I produced in a survey, which is long gone by now, asked a question which was guaranteed to get a certain answer, as timing raised a significant issue. If you are to base a survey off of the mood of your interviewees, then it would be smartest to compare the results that you got on every day of the week, as we all feel much happier on a Friday afternoon compared to a Monday morning. I have learned throughout the course of this project how apply my skills and interests to innovating a question which will impact my learning. Perhaps it will even impact those around me, and their mindset. All of my in-class time was spent working on this project, and I followed suite ethically. At the beginning of this project, I set a goal for myself to complete this entire project in class time. I succeeded in this, although I did decide to take it a step further outside of class time.
Communicating and Representing
Represent mathematical ideas in concrete, pictorial, and symbolic forms
The final product which represented this core competency was the survey result graphs. Both of these graphs were created using knowledge that I did not have before the project, such knowledge of a sample survey, or a representative population. Both of these graphs were created using scatter plots, a graph that specifically shows a trend between survey results. To extend our learning, Meg had the fabulous idea of using failed surveys to search for a correlation. Talk about using your mistakes. We ended up taking results from a standardized test that I had conducted earlier on, and Meg’s graph that shows your belief in the Mandela effect. By combining these two very random facts together, we were able to spot a trend between the two. I am especially proud of this graph, as it shows how we have taken our knowledge of the subject a step further.
Communicating and Representing:
Use mathematical vocabulary and language to contribute to mathematical discussions
Verbal and visual explanations were offered to help the audience understand the circumstances where both correlation and causation are present. I spent a good amount of time creating the visuals for this project for an aesthetically pleasing outcome. Something that I could have improved upon was properly explaining how correlation could be taken for causation. I fully comprehend this idea, but I failed to include it prominently in our conversation. We offered a logical explanation for causation in our first graph.
I created this checklist in case you ever find yourself in predicament and need a causation checklist.
Thank you for attending my TED talk.
“Good morning, and in case I don’t see ya, good afternoon, good evening and goodnight.” – The Truman Show