The COVID-19 landscape is bombarding us all with unprecedented amounts of data. With so many graphs, charts and percentages flooding our screens on various news channels, websites and social channels, it can be very easy to fall into common ‘data pitfalls’ – misunderstandings over what the data actually means.
From epistemic errors and mathematical miscues, to analytical aberrations and graphical gaffes, there are in fact seven main categories of data pitfalls that these can fall into.
It is essential that, given the current health climate, we are able to accurately digest and understand what the data being served is telling us and how best to communicate it to others.
That’s why I have unpacked the seven data pitfalls in a recent video, ‘Avoiding data pitfalls – the COVID-19 edition’. The goal is not to embarrass anyone by calling out their mistakes, but rather to highlight that there are very common and easy misassumptions made when working with data.
It is worth noting, that like many people discussing the data behind COVID-19 headlines, I am not an expert in public health or epidemiology.
My insights are based on the checklist I created in my book ‘Avoiding Data Pitfalls’, which explores common data blunders; how they arise, how they have become so common, and how you can avoid them from the outset.
In the video, I have highlighted how these can be used to better understand COVID-19 data, but the checklist can be utilized anytime you are handling and working with data across your personal and professional lives going forward.
I hope everyone is staying safe and please feel free to let me know your thoughts on the video, via the comments section on YouTube or via Twitter: @DataRemixed.