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to well-suit. It's a Windows error that returns a dialog about ABIOS (Basic I/O) Subsystems, harry enten indicating invalid entries and corrupted drivers. Despite their obscurity to
Nate Silver
most of us, these are actually common and analogous issues in developing data projects for journalism…corrupted, 538 polls dated, or invalid info being problematic in both cases. This is a post about one of those cases. If you've been following journalistic tracking of the
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Nigeria kidnappings, then you might have come across 538, a collective of hackers and journalists who has been reporting on the topic and recently posted this set of maps using GDELT (Global Database of Events Language and Tone) data. This garnered a series of pretty solid rebuttals about integrity of their assertions; donald trump polls see @charlie_simpson's Storify feed and Daniel Solomon on Source. The problem with the piece in question (to summarize the previous links), is that it provides time-series and mapped analysis of kidnapping in Nigeria but skews representation of the actual data plotted. http://fivethirtyeight.com/datalab/mapping-kidnappings-in-nigeria/ http://fivethirtyeight.com/datalab/mapping-kidnappings-in-nigeria/ As someone who works with journo orgs, crowdsourced crisis-mapping projects, data, and Africa I thought I'd comment on some of the fallibilities briefly. The particular fumbles I see in the 538 representation of kidnapping incidents in Nigeria can be bundled under three issues that are persistently problematic in all data journalism projects. ISSUE 1: REPRESENTATIONAL INTEGRITY A lot of issues with data mapping/graphing projects boil down to human representational error: what is your map actually showing and what are you saying it's showing? In this case, the equivalence of GDELT media data and actu
Donald Trump during a campaign event at the U.S. Cellular Convention Center on Feb. 1 in Cedar Rapids, Iowa. Getty Images
Hillary Clinton
Donald Trump during a campaign event at the U.S. Cellular Convention Center
Bernie Sanders
on Feb. 1 in Cedar Rapids, Iowa. Joshua Lott / Getty Images How I Acted Like A Pundit 538 politics And Screwed Up On Donald Trump Trump's nomination shows the need for a more rigorous approach. By Nate Silver Filed under 2016 Election Published May 18, 2016 EmailTwitterFacebook Since Donald https://aureliamoser.com/2014/05/15/538-errors-plotting-crises-and-the-protocol-of-reprocessing-data/ Trump effectively wrapped up the Republican nomination this month, I’ve seen a lot of critical self-assessments from empirically minded journalists -- FiveThirtyEight included, twice over -- about what they got wrong on Trump. 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 http://fivethirtyeight.com/features/how-i-acted-like-a-pundit-and-screwed-up-on-donald-trump/ to the news. That means observing the world, formulating hypotheses about it, and making those hypotheses falsifiable. (Falsifiability is one of the big reasons we make predictions.1) When those hypotheses fail, you should re-evaluate the evidence before moving on to the next subject. The distinguishing feature of the scientific method is not that it always gets the answer right, but that it fails forward by learning from its mistakes. But with some time to reflect on the problem, I also wonder if there’s been too much #datajournalist self-flagellation. Trump is one of the most astonishing stories in American political history. If you really expected the Republican front-runner to be bragging about the size of his anatomy in a debate, or to be spending his first week as the presumptive nominee feuding with the Republican speaker of the House and embroiled in a controversy over a tweet about a taco salad, then more power to you. Since relatively few people predicted Trump’s rise, however, I want to think through his nomination while trying to avoid the seduction of hindsight bias. What sho
election? A User’s Guide To FiveThirtyEight's 2016 General Election Forecast By Nate Silver Filed under 2016 Election Published Jun 29, 2016 EmailTwitterFacebook We’ve just launched FiveThirtyEight's 2016 general election forecast, which projects http://fivethirtyeight.com/features/a-users-guide-to-fivethirtyeights-2016-general-election-forecast/ how the 538 Electoral College votes could break down in the presidential election. The forecast will be continually updated through Election Day on Nov. 8. Here’s a bullet-point-style look at how it was built. What’s new in the model since 2012? Not that much! It’s mostly the same model as the one we used to successfully forecast the 2008 and 2012 elections. There’s no special variable for http error Republican Donald Trump or Democrat Hillary Clinton. They’re treated the same as any other candidates would be with the same polling numbers. We built procedures to handle Libertarian Gary Johnson and other third-party candidates. We double-checked lots of assumptions and code. We’re now showing different versions of the model: the polls-only and polls-plus forecasts, and the now-cast (what would happen in an election held today). Major themes http error 538 and findings Think probabilistically. Our probabilities are based on the historical accuracy of election polls since 1972. When we say a candidate has a 30 percent chance of winning despite being down in the polls, we’re not just covering our butts. Those estimates reflect the historical uncertainty in polling. State polls > national polls. All versions of our models gain more information from state polls than from national polls. Errors are correlated. But while the election is contested at the state level, the error is correlated from state to state. If a candidate beats his polls to win Ohio, there’s a good chance he’ll also do so in Pennsylvania. Be conservative early and aggressive late. Fluctuations in the polls in the summer are often statistical noise or short-term bounces. The model is trained to be conservative in reacting to them. Fluctuations late in the race are more meaningful, and the model will be more aggressive. Three versions of the model Polls-plus: Combines polls with an economic index. Since the economic index implies that this election should be a tossup, it assumes the race will tighten somewhat. Polls-only: A simpler, what-you-see-is-what-you-get version of the model. It assumes current polls reflect t