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The quest for balance in a health news story can fail before the first sentence if the headline isn't appropriately calibrated. With that in mind, I looked at news stories and releases that we reviewed over the past month and compared the headline message with that of the study on which the news is based. About a third of news story headlines and a quarter of news release headlines either misstated the results or went beyond what the research could support.

During the 2016 EU referendum campaign, both sides used statistics pretty freely to back their arguments. Understandably, UK broadcasters felt compelled to balance competing perspectives, giving audiences the opportunity to hear the relative merits of leaving or remaining in the EU. In doing so, however, the truth of these statistical claims was not always properly tested. This might help explain some of the public's misconceptions about EU membership. So, for example, although independent sources repeatedly challenged the Leave campaign's claim that the UK government spent £350m per week on EU membership, an IPSOS MORI survey found that almost half of respondents believed this was true just days before the election. Of the 6,916 news items examined in our research, more than 20% featured a statistic. Most of these statistical references were fairly vague, with little or limited context or explanation. Overall, only a third provided some context or made use of comparative data. Statistics featured mostly in stories about business, the economy, politics and health. So, for example, three-quarters of all economics items featured at least one statistic, compared to almost half of news about business. But there were some areas - where statistics might play a useful role in communicating trends or levels of risk - that statistics were rarely used.

Great article discussing experts, their role in democracy, and some of the problems facing expertise. "Part of the problem is that some people think they're experts when in fact they're not. We've all been trapped at a party where one of the least informed people in the room holds court, confidently lecturing the other guests with a cascade of banalities and misinformation. This sort of experience isn't just in your imagination. It's real, and it's called "the Dunning-Kruger effect," after the research psychologists David Dunning and Justin Kruger. The essence of the effect is that the less skilled or competent you are, the more confident you are that you're actually very good at what you do. The psychologists' central finding: "Not only do [such people] reach erroneous conclusions and make unfortunate choices, but their incompetence robs them of the ability to realize it." We are moving toward a Google-fueled, Wikipedia-based collapse of any division between professionals and laypeople."

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.

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.

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.

Since the census is the ultimate measure of population in the U.S., one might wonder how we could even know if its count was off. In other words, who recounts the count? Well, the Census Bureau itself, but using a different data source. After each modern census, the bureau carries out research to gauge the accuracy of the most recent count and to improve the survey for the next time around. The best method for determining the scope of the undercount is refreshingly simple: The bureau compares the total number of recorded births and deaths for people of each birth year, then adds in an estimate of net international migration and … that's it. With that number, the bureau can vet the census - which missed 4.6 percent of kids under 5 in 2010, according to this check.

Interesting links to different sources discussing data on teacher pay, and what it shows in relation to different things that stakeholders might care about. educator pay varies significantly across states, from an average of $44,921 in Oklahoma to $77,957 in New York. Why should school administrators and government leaders care about teacher pay - beyond wanting their employees to be able to afford their living expenses? Below, we present research that examines this issue. What scholars have found is that teacher salaries are linked to employee retention and that better pay seems to draw smarter people to the field and into the classroom. It's not clear, however, whether higher salaries result in higher student achievement.

A great data-story on the gender imbalance worldwide, using data and research to investigate and highlight key issues. This piece in the economist takes a different approach to using the data https://www.economist.com/node/15636231 "In 1960, the earliest year the World Bank provides data for, the world was within 0.002 percentage points of a perfectly equal distribution. Ever since, the gap has widened; now men outnumber women on the planet by more than 66 million. When this piece was first published in early 2014, the gap had already been the widest ever - the trend continues."

Most of the poor people in the world are women. In no country on earth are women economically equal to men, and Australia is no exception. Research from Acoss and the University of New South Wales last year showed that a higher share of people living in poverty in Australia are women. The experience of living below the breadline in our very wealthy nation is a gendered one, for reasons that are complex and intertwined. As women progress through life, they encounter a series of barriers and setbacks that simply do not encumber men in the same way. The cause of gendered poverty is structural. It is entrenched in our workplace settings, and embedded in our personal relationships. It is at play at every stage of a woman's life, from childhood to the grave, making its mark on our education, our employment, our homes, our familial responsibilities and our retirement options. At its heart is the simple fact that women do the lion's share of caring for others. Caring is women's work, and our society does not value women's work.

Is income inequality growing (in America)? This article discusses the issue, and uses a chart from the Financial Times (and Pew research data) to demonstrate the change since 1971. Great visualisation and great discussion of what it shows.

Great podcast from our own 2ser & UTS Business School! "latest episode of #ThinkBusinessFutures @2ser, with Dr Geetanjali Saluja @UTSMarketing @UTS_Business, discussing her research into moral decision making, and Adam Ferrier, author of 'The Advertising Effect: How to Change Behaviour'" Discusses some of the framing, cognitive bias, and their impact on decision making that we talked about in class

journalists often write about public opinion polls, which are designed to measure the public's attitudes about an issue or idea. Some of the most high-profile polls center on elections and politics. Newsrooms tend to follow these polls closely to see which candidates are ahead, who's most likely to win and what issues voters feel most strongly about. Other polls also offer insights into how people think. For example, a government agency might commission a poll to get a sense of whether local voters would support a sales tax increase to help fund school construction. Researchers frequently conduct national polls to better understand how Americans feel about public policy topics such as gun control, immigration reform and decriminalizing drug use. When covering polls, it's important for journalists to try to gauge the quality of a poll and make sure claims made about the results actually match the data collected. Sometimes, pollsters overgeneralize or exaggerate their findings. Sometimes, flaws in the way they choose participants or collect data make it tough to tell what the results really mean. Below are 11 questions we suggest journalists ask before reporting on poll results. While most of this information probably won't make it into a story or broadcast, the answers will help journalists decide how to frame a poll's findings - or whether to cover them at all.

"I am quite sure now that often, very often, in matters of religion and politics a man's reasoning powers are not above the monkey's," wrote Mark Twain. Having written a book about our most common reasoning errors, I would argue that Twain was being rather uncharitable - to monkeys. Whether we are discussing Trump, Brexit, or the Tory leadership, we have all come across people who appear to have next to no understanding of world events - but who talk with the utmost confidence and conviction. And the latest psychological research can now help us to understand why.

"Anonymised" data lies at the core of everything from modern medical research to personalised recommendations and modern AI techniques. Unfortunately, according to a paper, successfully anonymising data is practically impossible for any complex dataset.

Almost one in four cancers detected in men were overdiagnosed in 2012, according to our new research, published today in the Medical Journal of Australia. In the same year, we found that approximately one in five cancers in women were overdiagnosed. Overdiagnosis is when a person is diagnosed with a "harmless" cancer that either never grows or grows very slowly. These cancers are sometimes called low or ultra-low-risk cancers and wouldn't have spread or caused any problems even if left untreated. Cancer overdiagnosis can result in people having unnecessary treatments, such as surgery, radiotherapy and hormone therapy. Being diagnosed with cancer and having cancer treatments can cause physical, psychological and financial harms.

But because a machine learning model learns the boundaries of its world from its input data, just three people informed how any model using that dataset would interpret if 'childbirth' was emotional. This led to a perspective that has informed all of my work since: a dataset is a worldview. It encompasses the worldview of the people who scrape and collect the data, whether they're researchers, artists, or companies. It encompasses the worldview of the labelers, whether they labeled the data manually, unknowingly, or through a third party service like Mechanical Turk, which comes with its own demographic biases. It encompasses the worldview of the inherent taxonomies created by the organizers, which in many cases are corporations whose motives are directly incompatible with a high quality of life.

"Fitness trackers out of step when measuring calories, research shows," The Guardian reports. An independent analysis of a number of leading brands found they were all prone to inaccurate recording of energy expenditure.

Cool study showing the potential of data analyses to give us new kinds of insights! What does instagram tell us about peoples' nutrition decisions? Food deserts, or places where people have limited access to fresh food, are usually measured by the distance people have to travel to get to a large grocery store. What's harder to measure is what the residents of these areas are actually eating day to day. To do so, researchers typically have to rely on surveys, ...In a recent study, De Choudhury and her colleagues propose another method: mining Instagram. All those artfully arranged plates, all that latte art, just waiting for someone to analyze it!

There's a huge debate going on in social science right now. The question is simple, and strikes near the heart of all research: What counts as solid evidence? The answer matters because many disciplines are currently in the midst of a "replication crisis" where even textbook studies aren't holding up against rigorous retesting. ...