Nature's Amy MaxMen on the achievements of Gapminder's Hans Rosling

In January 2011 and June 2013, I linked to two videos by Swedish statistician and popularizer Hans Rosling demonstrating different demographic trends. Today, via 3 Quarks Daily, I came across Amy Maxmen's excellent long-format article on Rosling and his accomplishments, "Three minutes with Hans Rosling will change your mind about the world". It does a great job of explaining just what Rosling, and his Gapminder Foundatin, are trying to achieve, and why.

Back in Sweden, Rosling continued to teach global health, moving to the Karolinska Institute in Stockholm in 1996. But he came to realize that neither his students nor his colleagues grasped extreme poverty. They pictured the poor as almost everyone in the ‘developing world’: an arbitrarily defined territory that includes nations as economically diverse as Sierra Leone, Argentina, China and Afghanistan. They thought it was all large family sizes and low life expectancies: only the poorest and most conflict-ridden countries served as their reference point. “They just make it about us and them; the West and the rest,” Rosling says. How could anyone hope to solve problems if they didn’t understand the different challenges faced, for example, by Congolese subsistence farmers far from paved roads and Brazilian street vendors in urban favelas? “Scientists want to do good, but the problem is that they don’t understand the world,” Rosling says.

Ola, his son, offered to help explain the world with graphics, and built his father software that animated data compiled by the UN and the World Bank. Visual aids in hand, the elder Rosling began to script the provocative presentations that have made him famous. In one, a graph shows the distribution of incomes in 1975 — a camel’s back, with rich countries and poor countries forming two humps. Then he presses ‘go’ and China, India, Latin America and the Middle East drift forward over time. Africa moves ahead too, but not nearly as much as the others. Rosling says, “The camel dies and we have a dromedary world with one hump only!” He adds, “The per cent in poverty has decreased — still it’s appalling that so many remain in extreme poverty.”

Rosling’s online presentations grew popular, and the investment bank Goldman Sachs invited him to speak at client events. His message seemed to support advice from the firm’s chief economist, Jim O’Neill. In 2001, O’Neill had coined the acronym BRIC for the emerging economies of Brazil, Russia, India and China, often considered part of the developing world. He warned that financial experts ignored these rising powers at their peril. “I used to tease my colleagues who thought in a traditional framework,” O’Neill says. “Why are we talking about China as the developing world? Based on the rate of economic growth, China creates another Greece every three months; another UK every two years.”

Rosling welcomed the new audience. “They request my lectures because they want to know the world as it is,” he says. The private sector needs to understand the economic and political conditions of current and potential markets. “To me it was horrific to realize that business leaders had a more fact-based world view than activists and university professors.”

[. . .]

Rosling’s charm appeals to those frustrated by the persistence of myths about the world. Looming large is an idea popularized by Paul Ehrlich, an entomologist at Stanford University in California, who warned in 1968 that the world was heading towards mass starvation owing to overpopulation. Melinda Gates says that after a drink or two, people often tell her that they think the Gates Foundation may be contributing to overpopulation and environmental collapse by saving children’s lives with interventions such as vaccines. She is thrilled when Rosling smoothly uses data to show how the reverse is true: as rates of child survival have increased over time, family size has shrunk. She has joined him as a speaker at several high-level events. “I’ve watched people have this ‘aha’ moment when Hans speaks,” she says. “He breaks these myths in such a gentle way. I adore him.”

Here's another clip, a video taken last year where Rosling explains the reality of a strong convergence of Mexico with the United States.

"Trump's Win Isn't the Death of Data--It Was Flawed All Along"

I'm going to react at greater length and in greater detail to the surprise outcome of the American presidential election. In the meantime, I'd like to point readers to Cade Metz's Wired article "Trump's Win Isn't the Death of Data--It Was Flawed All Along". It raises a lot of interesting questions about statistics collection generally, not just political polling.

The lesson of Trump’s victory is not that data is dead. The lesson is that data is flawed. It has always been flawed—and always will be.

Before Donald Trump won the presidency on Tuesday night, everyone from Nate Silver to The New York Times to CNN predicted a Trump loss—and by sizable margins. “The tools that we would normally use to help us assess what happened failed,” Trump campaign reporter Maggie Haberman said in the Times. As Haberman explained, this happened on both sides of the political divide.

Appearing on MSNBC, Republican strategist Mike Murphy told America that his crystal ball had shattered. “Tonight, data died,” he said.

But this wasn’t so much a failure of the data as it was a failure of the people using the data. It’s a failure of the willingness to believe too blindly in data, not to see it for how flawed it really is. “This is a case study in limits of data science and statistics,” says Anthony Goldbloom, a data scientist who once worked for Australia’s Department of Treasury and now runs a Kaggle, a company dedicated to grooming data scientists. “Statistics and data science gets more credit than it deserves when it’s correct—and more blame than it deserves when it’s incorrect.”

With presidential elections, these limits are myriad. The biggest problem is that so little data exists. The United States only elects a president once every four years, and that’s enough time for the world to change significantly. In the process, data models can easily lose their way. In the months before the election, pollsters can ask people about their intentions, but this is harder than it ever was as Americans move away from old-fashioned landline phones towards cell phones, where laws limit such calls. “We sometimes fool ourselves into thinking we have a lot of data,” says Dan Zigmond, who helps oversee data science at Facebook and previously handled data science for YouTube and Google Maps. “But the truth is that there’s just not a lot to build on. There are very small sample sizes, and in some ways, each of these elections is unique.”