Bullshit and Climate Change
One of the most debated topics that I find to be a pretty good example of bullshit would be arguments refuting the impact of global climate change. For this assignment, I researched posts that go against the claims that global warming is real, and the basis of the explanations and data visualizations I found generally revolved around how it’s natural for earth to cycle from patterns of intense heat to cooling. I was interested in seeing if I could do my own exploratory data analysis to help prove the existence of climate change from statistical geographical temperature data. Here are some of the visualizations I’ve found that can help refute the impact of climate change:
From these two charts above, you can see the most popular counter arguments that I’ve found, showing that heat patterns naturally cycle from a state of heating to cooling, and that there is no direct impact by human interaction. One of the issues I have with this first chart is that it is not labeled very well and the timeline on the x-axis appears to be very short in terms of showing change over a long period of time. The second chart appears to skew data from continent heating patterns by using specific land temperature data from a small sample of continents with a relatively short timeline. I believe this could be an example of bullshit as this involves a particular type of bluff, as it requires the intention of skewing data plots and ignoring counter arguments that do not pursue the narrative of refuting scientific consensus regarding CO2 emissions and the impact to earth’s atmosphere from climate change.
One interesting aspect I found while researching this topic is how misinformation or bullshit can be widely spread as reliable evidence across the internet even if it is only a calculated bluff in order to help push a specific narrative. For example, this article, (https://theconversation.com/i-was-an-exxon-funded-climate-scientist-49855) “I Was An Exxon-funded Climate Scientist” which highlights the possibility for information to be purposefully skewed or an act of fakery in order to collect financial compensation by powerful corporations that wish to push a specific narrative regardless of the objective truth.
In order to find a testable solution for these claims above, you could use EDA to refute the data visualizations above by using a large dataset which contains information regarding natural weather patterns and climate changing indicators such as temperature change across a large range of geographical locations. Luckily for us, Kaggle contains such data! To begin, I downloaded the available dataset from this link (https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data) and set out to see if I can do my own exploratory data analysis to try and refute the claims above and try to help prove that climate change should be considered a relatively objective truth.
To start, I loaded in the .csv file into a python file and inspected the contents of what the dataset had, then from there I used a simple matplotlib data visualization across the average global temperature by year from 1750 to the end of the dataset which is around 2015.
From the data visualization above, you can see the temperature increase much clearer compared to the prior charts shown. You can see that there is a pretty strong variation across early years, but then there becomes a steady increase around late 1900’s and early 2000’s in which temperature begins to increase exponentially. By using exploratory data analysis, an individual is capable of diving deeper into misinformed or confusing topics by understand the issue by the objective data in order to bypass potential subjective interpretation skewing the reality of a situation. Being able to spot out and identify bullshit out in the wild can be a difficult task at times, but using EDA will allow you to attain a new perspective by the data.
There has to be a specific lack of concern for the truth in order to achieve the categorization of bullshit, and by suspending the big picture of a situation and only using narrow and oddly specific data to help pursue a narrative I believe would fall close in that category. On the other hand, I can also see how using EDA could also amplify a false narrative by refuting other ideas by specific data analysis to convey a form of confirmation bias. I think the important lesson that I’ve gained from this assignment is that there can be a lot of bullshit in the wild which can spread false narratives which could easily be figured out objectively by using EDA techniques to try and learn the truth for yourself.
