UTS-AEI

The "What's going on in this graph' series is from the NYT Learning Network, and is about interpreting graphs that represent real data to tell a story. It's aimed at high school students but that just means the examples and explanations are a really great introduction to visualising and interpreting data!

Life is one long string of decisionmaking, even if none of them is major. This is certainly the impression we get from reading cognitive neuroscience journals. A great many studies these days seem to involve 'decisionmaking under uncertainty' (otherwise known as gambling). As a married couple, we have now clocked up just over 50 years of decisionmaking together. We still frequently avoid or delay decisions, but we know that this does not pay off in the long run. And, when we do make decisions, we usually make them jointly. In case this sounds too good to be true, we hasten to add that it's not always easy and often involves arguments - despite, or perhaps because, we are both cognitive neuroscientists ourselves. Actually, argument turns out to be a well-kept secret in group decisionmaking. But before we turn to the value of acrimony, let's look at some of the reasons why we believe that people can make better decisions together than they can on their own.

As an epidemiologist, I am interested in disease - and more specifically, who in a population currently has or might get that disease. What is their age, sex, or socioeconomic status? Where do they live? What can people do to limit their chances of getting sick? Questions exploring whether something is likely to happen or not can be answered with quantitative research. By counting and measuring, we quantify (measure) a phenomenon in our world, and present the results through percentages and averages. We use statistics to help interpret the significance of the results. While this approach is very important, it can't tell us everything about a disease and peoples' experiences of it. That's where qualitative data becomes important.

Interesting discussion of data on vote recounts and using electronic or hand counting methods (in America where they use electronic voting machines quite commonly). These numbers represent three main kinds of disputes, Foley told me. First, candidates (and their lawyers) argue over what ballots should be counted and which should be thrown out as ineligible. Then, they argue over which candidate specific ballots should count for. Finally, they argue over whether all the eligible votes were counted correctly - the actual recount. Humans are much better than machines at making decisions around the first two kinds of ambiguous disputes, Stewart said, but evidence suggests that the computers are better at counting. Michael Byrne, a psychology professor at Rice University who studies human-computer interaction, agreed. "That's kind of what they're for," he said.

Journalists often make mistakes when reporting on data such as opinion poll results, federal jobs reports and census surveys because they don't quite understand - or they ignore - the data's margin of error. Data collected from a sample of the population will never perfectly represent the population as a whole. The margin of error, which depends primarily on sample size, is a measure of how precise the estimate is. The margin of error for an opinion poll indicates how close the match is likely to be between the responses of the people in the poll and those of the population as a whole. To help journalists understand margin of error and how to correctly interpret data from polls and surveys, we've put together a list of seven tips, Look for the margin of error - and report it. It tells you and your audience how much the results can vary. Remember that the larger the margin of error, the greater the likelihood the survey estimate will be inaccurate. Make sure a political candidate really has the lead before you report it. Note that there are real trends, and then there are mistaken claims of a trend. Watch your adjectives. (And it might be best to avoid them altogether.) Keep in mind that the margin of error for subgroups of a sample will always be larger than the margin of error for the sample. Use caution when comparing results from different polls and surveys, especially those conducted by different organizations.

An excellent online course from the Open Data Institute on working with data to find stories

Uses the example of health treatments to illustrate some of the problems with using the average

Here's a discussion of how data science techniques that look at the kinds of things you watch, and try and make recommendations or customise based on that can work, and what might be problematic about that. "Some are noticing Netflix's tendency to entice black users with movie posters featuring black actors, no matter how minor their role in the film."

In what feels like an increasingly polarised world, trying to convince the "other side" to see things differently often feels futile. Psychology has done a great job outlining some of the reasons why, including showing that, regardless of political leanings, most people are highly motivated to protect their existing views."Our results show why debates about controversial issues often seem so futile," the researchers said. "Our values can blind us to acknowledging the same logic in our opponent's arguments if the values underlying these arguments offend our own."

Different ways to use visualisation to gain insight onto the same issue (the US midterm elections)

McDonald's Monopoly competition is back this month offering a chance to win expensive prizes, all for the price of a Big Mac. Given you could become tens of thousands of dollars richer by simply going on a Macca's run, McDonald's Monopoly games have in the past been subject to cheating and a multimillion-dollar scandal. But for those who prefer to play fair, what are your chances of actually snaring a prize?

Excellent discussion of absolute and relative risk in the context of alcohol safety. "But claiming there is no 'safe' level does not seem an argument for abstention. There is no safe level of driving, but government do not recommend that people avoid driving."

When Uber and Lyft first entered the market, offering a ride-hailing service that would come to include tens of thousands of amateur drivers, most major American cities had been tightly controlling the competition. New York City allowed exactly 13,637 licenses for taxicabs. Chicago permitted 6,904, Boston 1,825 and Philadelphia 1,600. These numbers weren't entirely arbitrary. Cities had spent decades trying to set numbers that would keep drivers and passengers satisfied and streets safe. But the exercise was always a fraught one. And New York City now faces an even more complex version of it, after the passage of legislation this week that will temporarily cap services like Uber and Lyft. The city plans to halt new licenses for a year while it studies the impact of ride-hailing and establishes new rules for driver pay. In doing so, it renews an old question: What's the right number of vehicles anyway? The answer isn't easy because it depends largely on which problem officials are trying to solve. Do they want to minimize wait times for passengers or maximize wages for drivers? Do they want the best experience for individual users, or the best outcome for the city - including for residents who use city streets but never ride taxis or Uber at all?

Interesting look at data around private healthcare and marketisation. Each year, for well over a decade, more people have faced higher health insurance deductibles. The theory goes like this: The more of your own money that you have to spend on health care, the more careful you will be - buying only necessary care, purging waste from the system. But that theory doesn't fully mesh with reality: High deductibles aren't working as intended. A body of research - including randomized studies - shows that people do in fact cut back on care when they have to spend more for it. The problem is that they don't cut only wasteful care. They also forgo the necessary kind. This, too, is well documented, including with randomized studies. People don't know what care they need, which is why they consult doctors.

Why do arguments change people's minds in some cases and backfire in others? Hugo Mercier explains how arguments are more convincing when they rest on a good knowledge of the audience, taking into account what the audience believes, who they trust, and what they value.

In August 2016, the Australian government released an "anonymised" data set comprising the medical billing records, including every prescription and surgery, of 2.9 million people. Names and other identifying features were removed from the records in an effort to protect individuals' privacy, but a research team from the University of Melbourne soon discovered that it was simple to re-identify people, and learn about their entire medical history without their consent, by comparing the dataset to other publicly available information, such as reports of celebrities having babies or athletes having surgeries.

People use imprecise words to describe the chance of events all the time - "It's likely to rain," or "There's a real possibility they'll launch before us," or "It's doubtful the nurses will strike." Not only are such probabilistic terms subjective, but they also can have widely different interpretations. One person's "pretty likely" is another's "far from certain." Our research shows just how broad these gaps in understanding can be and the types of problems that can flow from these differences in interpretation.

Nice discussion of the data around contraception choices. Shortly after Donald Trump was elected president I started noticing an interesting trend on my social media newsfeeds. And no, I'm not talking about the near-constant bickering of people with differing political opinions. I started seeing post after post from friends publicly asking one another about their experiences with different forms of birth control. The motivation for these kinds of conversations centered around the pending rollback of copay-free contraception, but have since been re-kindled every time reproductive rights come up in the political arena. And it's not just talk. Many of these conversations centered around the use of long-term contraceptives like intra-uterine devices or IUDs which can protect against pregnancy for 3 - 12 years. In the months immediately following the 2016 election, AthenaHealth reported a 19% increase in IUD-related doctor's visits and Planned Parenthood reported a 900% increase in patients seeking IUDs. Cait, 27, recently switched to a copper IUD, and said that she made the switch due to convenience and "because now in light of our current administration I'd like to have something that will continue to work and be affordable even if I end up without health insurance."

Two Words That Could Shape the Politics of the Trade War: Loss Aversion The pain of a loss tends to be greater than the enjoyment of a win. That has big implications for trade, and also helps explain the politics of health care and taxes.

Interesting discussion of why headlines like this one "85% accurate" for the detection of stomach cancer" about an experimental breath test are problematic (because some people who don't have the condition get diagnosed with it, and they can miss people who genuinely do have the condition!). Good example using pregnancy tests as an infographic.