[From Unsplash]
Good morning,
In How to Make the World Add Up, Tim Harford tells the fascinating story of a doctor who set out to measure human beings’ normal body temperature. The story goes like this:
“Over the course of eighteen years, the nineteenth-century German doctor Carl Wunderlich assembled over a million measurements of body temperature, gathered from more than 25,000 patients. A million measurements! It’s a truly staggering achievement given the pen-and-paper technology of the day. Wunderlich is the man behind the conventional wisdom that normal body temperature is 98.6°F. Nobody wanted to gainsay his findings, partly because the dataset was large enough to command respect, and partly because the prospect of challenging it with a bigger, better dataset was intimidating. As Dr. Philip Mackowiak, an expert on Wunderlich, put it, ‘Nobody was in a position or had the desire to amass a dataset that large.’
“Yet Wunderlich’s numbers were off; we’re normally a little cooler (by about half a Fahrenheit degree). So formidable were his data that it took more than a hundred years to establish that the good doctor had been in error.
“So how could so large a dataset be wrong? When Dr. Mackowiak discovered one of Carl Wunderlich’s old thermometers in a medical museum, he was able to inspect it. He found that it was miscalibrated by two degrees centigrade, almost four degrees Fahrenheit. This error was partly offset by Dr. Wunderlich’s habit of taking the temperature of the armpit rather than carefully inserting the thermometer into one of the bodily orifices conventionally used in modern times. You can take a million temperature readings, but if your thermometer is broken and you’re poking around in armpits, then your results will be a precise estimate of the wrong answer. The old cliche of ‘Garbage in, garbage out’ remains true no matter how many scraps of garbage you collect.”
Also read: Tim Harford on sensemaking and storytelling (From our archives)
Have a great day!
Electoral forecasting
The Indian electoral landscape is going through significant shifts. And journalists reporting from the ground, researchers crunching data, and pollsters attempting to predict election results are trying to decode how the Indian voter thinks. This is why we found an essay by Samarth Bansal on predicting the outcome of elections most compelling. He tackles questions like why most of us are interested in it and how ought it be approached.
“What exactly is the point of predicting an election a few days in advance? At the heart of this question lies another epistemic puzzle: How do we make sense of Indian elections? Why do people vote the way they do?
“Here is what I think: Deeply reported stories—however accurate—only show us part of the picture. We need credible, high-quality data to capture the bigger trends. Otherwise, we are at the mercy of talking heads and their various pet theories, often backed by very little evidence other than their skewed interpretation. We often see what we are already primed to see. Data—if deployed and assessed properly—helps correct and expand our vision.
“Data from Indian pollsters have revealed some significant shifts in Indian politics over the last few years. For example, in the recent state assembly elections, more women voted for the BJP than men in all four states the party won. This is a big turnaround: the BJP had a gender disadvantage with women before 2014. Polling data shows they closed it at the national level in 2019. And it’s now happening in state elections.
“Ground reporting can help us figure out why this is happening and its implications for upcoming campaigns, but we would not know the scale of these shifts without survey data.
“But surveys have one big problem: human beings are liars. We lie to our family, friends and colleagues. We lie to ourselves. And we lie to pollsters. People over-report how much sex they are having and under-report the number of cigarettes they are smoking. They pretend to have voted in elections and cheat on the calories they are consuming.
“So why should we trust data on electoral preferences? Do people have any incentive to reveal the truth? And what if the survey was done badly?
“That is precisely why election forecast matters: it proves or disproves the accuracy of a survey. A pollster’s prediction confronts the moment of truth on result day. A real test.
“In short, forecast matters for an accuracy check. Even high-stakes data like unemployment figures don’t have this privilege. We just have to accept the number surveys project, and if two surveys produce varying estimates, it’s hard to know which one is right. This is why I care about polls and forecasts.”
Dig deeper
Indian pollsters are doing fine. Here is how forecasts work
Effective altruism
In Vox, Dylan Matthews writes a deep and personal story on the evolution of effective altruism— “using evidence and reason to figure out how to benefit others as much as possible”—from a niche movement to a mainstream phenomenon.
He writes: “I encountered effective altruism while I was a journalist covering federal public policy in the US. I lived and breathed Senate committee schedules, think tank reports, polling averages, outrage cycles about whatever Barack Obama or Mitt Romney said most recently. I don’t know if you’ve immersed yourself in American politics like that … but it’s a horrible place to live. Arguments are more often than not made in extreme bad faith. People’s attention was never focused on issues that mattered most to the largest number of people. Progress for actual people in the actual world, when it did happen, was maddeningly slow…
“Finding EA was a… transformative experience for me… I now had a sense that there was more to the world than the small corner I had dug into in Washington, DC. This is, in retrospect, an obvious revelation. If I had spent this period as a microbiologist as CRISPR emerged, it would have been obvious that there was more to the world than US politics. If I had spent the period living in India and watching the world’s largest democracy emerge from extreme poverty, it would have been obvious too.
“But what’s distinctive about EA is that because its whole purpose is to shine light on important problems and solutions in the world that are being neglected, it’s a very efficient machine for broadening your world…
“My anxieties about EA’s evolution, as it tends toward longtermism and gets more political, are bound up in pride at that achievement, in the intelligent environment that the movement has fostered, and fear that it could all come crashing down. That worry is particularly pronounced when the actions and fortunes of a handful of mega-donors weigh heavily on the whole movement’s future. Small, relatively insular movements can achieve a great deal, but they can also collapse in on themselves if mismanaged.”
Dig deeper
How effective altruism went from a niche movement to a billion-dollar force
Just do it
(Via WhatsApp)
Warm regards,
Team Founding Fuel
