I like this article. To me, it even provides more evidence as to why Tukey's fences are not really the way to go for outlier detection (Probably not your intention). Especially when in the real world the data in itself is not a Gauss process I think in a case where you are analysing a sample of averages by all means do so (no pun intended!). In cases where you are testing for causality, be extremely careful. Just imagine a case where you have data that has high kurtosis, you could end up removing over 15% of your data. If you end up using Tukey's Fences as a form of outlier detection in such a scenario, how can one make a strong claim about causality after wiping out 15% of the data?? Again, good article.
The possibility of long-term climate change is linked to an increase in gases that trap heat in the atmosphere — called greenhouse gases. Greenhouse gases warm the Earth by absorbing energy and slowing the rate at which energy escapes to space. These greenhouse gases include carbon dioxide, methane, and nitrous oxide.
The “Climate Change 2013: The Physical Science Basis” in a global assessment of climate change science showed that human activity of carbon dioxide and other greenhouse gases is a primary driver of climate change. In 2018, Africa’s total emissions were 1.4 …
Economist | Graphic Designer. Passionate about anything data and statistics related. Doing my best to explain Africa using data, economics & infographics.