With a countdown song in the back of my head, we had one final project for math and science; correlation vs causation.

For those who don’t know, multiple data points have correlation when they have a similar trend and are caused by a similar underlying thing. An example would be ice cream sales and bike sales increasing during the summer. Both increase due to better, warmer weather.

 

Causation is when one point of data directly impacts another. When those bike sales increase, more people ride bikes. If there are more bikes on the road, trail, whatever, more bike tires will pop. The increase in tires popped is causes by an increase in bike ridden per day.

With Robin, we made a video talking about correlation vs causation. We used those exact examples, and two more we had to find ourselves. We looked at both arm span and height, and population size and life expectancy.

https://youtu.be/zyd5gfd0IVo

First was arm span vs height. We got data from both our classmates and data online. But how do you measure something like this? Well, from a previous experiment, we knew the height of our test subjects in CM, and we could get the arm span easily. We had the test subjects hold the meter stick in one hand WITHOUT compromising the test, and put another stick against the end of the first. Where the tip of the middle finger was is the length of your arm span. After this test we found that the average arm span was about 1-2 centimeters longer than the average height. So yes, there is a correlation between the two.

Next up; something I wasn’t expecting. But before that, the causation section. We chose to find a causation between the population of a country and the life expectancy of that country. We went to a website called Gapminder to find our data. Gapminder has a whole bunch of data from population to country age to the amount of people who have access to a computer. We found what we’re looking for.

We were expecting for life expectancy to decrease as population decreased. Not a lot, but as population increased, there would be less space for housing and farms, and the resources for the poor would decrease, thus they would die sooner. But we found the exact opposite. There are many reasons at play here, but we think that as population increases, more people Han improve the life expectancy by helping out with food, sharing resources and more. In turn, the population increases as life expectancy increases, because more people are alive at one time.

Correlation and causation are in our daily lives, but we barely notice them. These were just a couple examples of correlations and causations. Some have obvious reasons for existing, but you may have to dig deep to find the reasons for others.