Credit: FiveThirtyEight

Numbers and Responsibility

For those of you who have produced stories with data, you are probably familiar with the name Nate Silver. He’s the editor-in-chief at FiveThirtyEight, whose stories was a licensed feature of NYTimes until the blog was sold to ESPN. Silver was the journalist/statistician who predicted Obama’s win in 2012 while the majority of other news forecasts said otherwise. But for this election season, he, like many others, “acted like a pundit and screwed up on Donald Trump”. As he writes in his most recent reflection on the failure (for the lack of a better word) to predict Trump’s nomination:

This instinct to be accountable for one’s predictions is good since the conceit of “data journalism,” at least as I see it, is to apply the scientific method to the news. That means observing the world, formulating hypotheses about it, and making those hypotheses falsifiable.

Prediction might be the hardest use of data in journalism for it requires rigorous modeling and understanding of the subject matter. Unlike showing patterns from a line chart or creating narratives with a map, you are hoping to trudge into the somewhat unknown future, and back up yourself with a sound statistical methodology. The accountability associated with the predictions shows how much you know your materials and, as a result, how truthful your reporting is.

However, while predictions can be informative and even influential, it’s probably not the most important form of data journalism. It’s certainly not the only place where you bear the responsibility for the knowledge you disseminate to the public. In Silver’s article from 2014, he outlines (emphasis mine):

The other reason I say our election forecasts were overrated is because they didn’t represent the totality, or even the most important part, of our journalism at FiveThirtyEight. We also covered topics ranging from the increasing acceptance of gay marriage to the election of the new pope, along with subjects in sports, science, lifestyle and economics. Relatively little of this coverage entailed making predictions. Instead, it usually involved more preliminary steps in the data journalism process: collecting data, organizing data, exploring data for meaningful relationships, and so forth. Data journalists have the potential to add value in each of these ways, just as other types of journalists can add value by gathering evidence and writing stories.

By now you know many things as data journalists. You know where to find data, how to visualize the data in different forms and how to make something interactive out of it.

But to truly use the data to your advantage it is not enough to know the how, but also when. When is it necessary to visualize a dataset? When should something be clickable as opposed to just a static graph? When to include a dataset in the story, if at all?

To add values to the steps of collecting, organizing, exploring, and especially presenting data with a found pattern, ask yourself along the way how they are bringing you (and thus the people you write for) closer to the story. Your responsibility is to present something with honesty, and with impact.

Mean what you make. If it’s the collection of an original dataset, design the schema carefully and compare it with any precedents for holes in methodology. If it’s a form of visualization/sonification, make sure it actually matters to the story and minimizes, rather than introducing, biased understanding. If it’s a prediction, learn the statistics or work with someone who’s fluent in statistics before you broadcast any observation.Whatever you learn— whether it is coding in python to scrape hundreds of webpages, or shooting for VR— they should be in congruence with the intention to present the most meaningful story, not the most flashy one with little substance.

I strongly recommend reading the entire article on data journalism by Nate Silver.

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