Confirmation bias in data-journalism

Data journalism can simply be explained as the use of data as a tool to tell or explain a news story. It can be in a form of infographics, statistics, charts, graphs, etc.

So who are these data journalists and how to be one?

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The good news is that you don’t necessarily need to be part of a large corporate media, a developer or even a coder to be a data journalist. Although of course, working under large corporations like The Times has its own benefits in terms of having more budget and resources and having people with actual reporting experience and skill.

Even so, technically, anyone can be a data journalist. All you need is a web access and you’re settled.

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With websites like OECD Statistics, World Health Organization and UNData giving free public access to everyone, anyone can do it.

Now although everyone can do it, it is important to note that not everyone can do it well.

However, its major advantage could also be its major disadvantage. The fact that anyone can be a data-journalist could also pose as a potential threat for the society.

It is important to highlight that there is a false sense of impartiality with data journalism.  For example, have you ever purposely put in a lot of those complicated statistical, numerical data in your presentation just to make it looks more professional and credible? I know I have.

Now I’m not saying it’s wrong, we do it because many people (I’m guilty for this too) will actually fall for that trick.

They might assume that by reading all these numbers and seeing all these graphs, it must be true. Yes, the information presented might be true, but it doesn’t necessarily mean it’s not biased. As mentioned by Sarah Cohen from The New York Times, just because it’s data doesn’t mean it’s not subjective.

Like any other types of journalism, there is always a possibility of author’s bias. So instead of critically analysing the facts, consider their good and bad aspects, conclude it based on their pros and cons, and present it in a way that is unbiased, many authors engage in a confirmation bias. This happens where authors tend to search, interpret and collect the data in a way that confirms their prior beliefs.

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In short, it exists when the author wants what they believe to be true, which might lead to tainted or misleading results.

An example can be seen from one of Buzzfeed article, where they claimed that Democrats watch more porn than Republicans.

Guess where the source was from? Yup, PornHub.

So although it is a data-backed journalism, it is still an opinion journalism. So as we wade into the ocean of data journalism nowadays, let’s not forget that it is also important to be aware of what we can and cannot trust.

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