UTS-AEI

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.

There is a black hole in our knowledge of women and girls around the world. They are often missing from official statistics, and areas of their lives are ignored completely. So campaigners say - but what needs to be done?

"Between 2000 and 2015, nearly every African country improved childhood nutrition, especially in reducing stunted growth caused by malnutrition" .... " national averages do not tell the full story. In Kenya, for example, rates of wasting in children under 5 were below 6% on average nationwide in 2015, yet in certain regions plagued by several years of poor rains, crop failure and disease outbreaks, estimated levels of wasting reach as high as 28%."... "Such fine-grained insight brings tremendous responsibility to act. It shows governments, international agencies and donors exactly where to direct resources and support."..."This shows how crucial it is to invest in data. Data gaps undermine our ability to target resources, develop policies and track accountability. Without good data, we're flying blind. If you can't see it, you can't solve it."

Use of mean and median to analyse the paygap at The Guardian group. The gender pay gap at Guardian News & Media is 11.3% - calculated by mean hourly pay - the company has reported as part of the government's compulsory gender pay gap initiative.Across GNM, the median pay difference, which takes the mid-point when all wage rates are lined up from the biggest to smallest - which reduces the impact of one-off outliers - is wider, at 12.1%. The figure for non-editorial is 18.2%, and nearly 9% in editorial.

When Ice Cream Sales Rise, So Do Homicides. Coincidence, or Will Your Next Cone Murder You?

Short (10 minute) podcast "How to question dubious statistics in just a few short steps."....it's got some very British humour (sorry), but pretty good!

Advancement toward gender equality at work has slowed since the 1990s for three major reasons: people's attitudes stopped becoming more gender egalitarian, occupations stopped gender integrating, and the gender wage gap began decreasing at slower rates. Sociologist Paula England has called this phenomenon an "uneven and stalled" gender revolution, and there have been dozens of studies showing how the progress in gender equality experienced during and immediately after the feminist movement of the 1970s has not been sustained through the 1990s and 2000s. Does this stalled revolution play out in management positions, too? And if so, how? To explore this, I used data on full-time managers obtained from the U.S. Census and American Community Survey for the years 1980 and 2010 to examine three major factors that contribute to gender equality in the labor force: women's representation in management, the occupational gender segregation among managers, and the gender wage gaps that vary across managerial occupations.

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."

A list of "happy data stories" or stories related to the arts...including: Pup Inflation: Good Dogs Getting Better. "Oh, but for a much purer example of silly data journalism that I remembered as soon as I hit send, earlier this year I scraped and analyzed the dog ratings from the Twitter account WeRateDogs." -David Henry Montgomery Sneakin' Toward the Weekend, Workers Fill Roadways "How about using hourly traffic counting stations to show how people are cheating out of the office earlier and earlier on Fridays? Mental note: we're four years away from that link being older than my incoming freshmen. Also, note the wonder at phones able to receive email being someday widely available in the story." -Matt Waite

As Omicron surges through the community, getting the right answer from a Rapid Antigen Test (RAT) is not as straightforward as reading one or two lines off the kit. RATs are a convenient diagnostic tool to detect COVID virus fragments in nasal secretions or saliva. They are designed to be self-administered and give an answer in minutes. Detecting infection early is critical to preventing spread and allowing persons at risk of severe disease to get timely access to close monitoring and new life-saving therapies. As governments plan to distribute tens of millions of RAT kits to schools and workplaces in coming weeks to help Australians work and study safely, it is important that we understand how to best use this diagnostic tool to reduce transmission and unnecessary disruptions to our lives and economy.

Protests against racial injustice and the COVID-19 pandemic have exposed racial inequalities rife within social and economic systems around the world. Fed up with police brutality and systemic racism against African Americans and other racialised groups, people staged protests against racial injustice in all 50 states across the United States.Apart from these examples, however, there is surprisingly little data or discourse about the impact of the disease on racial and ethnic minorities in the rest of Europe. This silence speaks volumes about Europe's approach to racism.The vast majority of EU member states do not use the concept of race or ethnic origin in data collection, in spite of policies like the European Racial Equality Directive and the Employment Equality Directive which prohibit racial or ethnic discrimination. France outright prohibits it.Without disaggregated data, it is virtually impossible to quantify the extent of discrimination experienced by racial and ethnic groups or the impacts of COVID-19 on their lives.

The data seems to overwhelmingly point to a criminal justice system riven by racial bias. But, remarkably, it could be even more overwhelming than some studies make it seem. That's because of a statistical quirk called "collider bias," a kind of selection bias that means that the crime data that shows racial bias is, itself, biased by racist practices. If you thought crime data showed clear evidence of racism before, understanding how collider bias affects these analyses might make it even clearer.

By pulling this chart apart and then helping you put it back together, this story aims to help you understand: how quickly coronavirus is spreading in different countries; where Australia fits into the global picture; what we can learn from countries that appear to have curbed the rise of COVID-19; and what you can do to help keep Australians safe. But first, one concept that's vitally important to understanding a pandemic is exponential growth. This is a pattern viruses tend to initially follow, due to the way they're spread. The result is that what might seem like a small difference in the rate of growth can actually have enormous impacts on how many people are infected overall.

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.

Wrapping your head around the scale of a global pandemic is not easy, and the volume of stats and data can be bewildering. What, for instance, are we to make of the fact Australia recorded just 109 new cases in its daily count for April 6? Given this figure peaked at around 400 new cases per day, does this mean the rate of infection is now tapering off? And what, apart from sadness, are we to make of more gruesome statistics, such as the 969 COVID-19 deaths reported in a single day in Italy on March 27? To help interpret and understand the mountains of COVID-19 data, we'll look at five commonly used methods, and explain the pros and cons of each.

'It's important to me that I make my own decisions, but I often wonder how much they are actually influenced by cultural and societal norms, by advertising, the media and those around me. We all feel the need to fit in, but does this prevent us from making decisions for ourselves? In short, can I ever be a truly free thinker?' Richard, Yorkshire. While being the lone "captain of your soul" is a reassuring idea, the truth is rather more nuanced. The reality is that we are social beings driven by a profound need to fit in - and as a consequence, we are all hugely influenced by cultural norms.

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.

how could two groups of 'experts' come up with such different conclusions, given they broadly agree of the evidence? David Aaronovitch in the Times identified the critical underlying issue behind the ensuing conflict: whether we take an individual- or a population-based approach. Essentially, the authors point out that any absolute risks are small from an individual perspective, and may generally be cancelled out by the enjoyment of eating, and the bother of changing habits. But these small benefits can be important from a public-health, population-wide perspective, since a lot of people making a small change, that only reduces their risk by a personally-negligible amount, can add up to thousands fewer cases of disease. That's what has generated the disagreement. It can be perfectly reasonable for guidance to be given by authorities, and it can also be perfectly reasonable for individuals to ignore it. Both can be 'right'.

Teach people to think critically about claims and comparisons using these concepts, urge Andrew D. Oxman and an alliance of 24 researchers - they will make better decisions.

Welcome! This app will help train you to make more accurate, better-calibrated predictions. For each question, you'll be prompted to say what you think the answer is and how confident you are in that answer. You should try to give the right answer whenever you can, but your main goal is to be accurate about how confident you are in each answer. In other words, your goal is to be "well-calibrated," which means that when you say you're 50% confident, you're right about 50% of the time, and when you say you're 80% confident, you're right about 80% of the time, and so on. Nobody is perfectly calibrated, but some people are much better calibrated than others, and a variety of studies suggest you can improve your calibration with the sort of practice this app provides.