UTS-AEI

Until the first Abbott-Hockey budget in 2014, a statement of budget measures that disproportionately affect women was published at budget time. At times given different names, the first was delivered with the Hawke government's 1984 budget. In its foreword, then Prime Minister Hawke promised that "within the overall economic objectives of the government" important budget decisions would from then on be made "with full knowledge of their impact on women". These women's budget statements shed light on the impact of decisions that might have been thought to have little to do with gender, such as the Hawke government's reduction of tariffs on imports of clothing, textiles and footwear. The statement pointed out that two-thirds of the workers in these industries were women and that without special support for retraining (which was given) they would be disproportionately disadvantaged. Increasingly, and especially during the Rudd and Gillard governments, the statements made visible the economic impact of women's greater responsibility for unpaid care work.

Interesting article about how different kinds of data (like your social media data) can give insights into health, but don't have the same protections as health data

"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.

Nice discussion on privacy, "How much do you value your privacy? Does it bother you what social media companies, governments know about you - your money, your body?"

Once your business has started collecting and combining all kinds of data, the next elusive step is to extract value from it. Your data may hold tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes. During a 2009 interview, Google's Chief Economist Dr. Hal R.Varian stated, "The ability to take data-to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it-that's going to be a hugely important skill in the next decades." Fast forward to 2016 and many businesses would agree with Varian's astute assessment.

Whether it's quitting smoking, reducing alcohol intake or making healthier dietary choices, many of us have habits we'd like to change. But it's really hard to know which treatment path to take. To advise their patients on the best of course of action, doctors sometimes compare treatments using something called the "number needed to treat" (NNT). In deciding whether to embark on a course of treatment, NNT can help. But the term is easily misunderstood by patients, and doctors as well. So it's useful to break down what NNT means.

When talking about the gender pay gap people tend to talk about the median figure rather than the mean. The mean is calculated by adding up all of the wages of employees in a company and dividing that figure by the number of employees. This means the final figure can be skewed by a small number of highly paid individuals. The median is the number that falls in the middle of a range when everyone's wages are lined up from smallest to largest and is more representative when there is a lot of variation in pay.

My name is Mary. This is quite a popular girla name in many countries. So, since childhood I've rarely been surprised to meet my namesake in daily life. But movies were a different matter! However, after hundred of watched movies and TV series I assumed that most of the characters had pretty ordinary names as well. James Bond, Jeff Lebowski and Sarah Connor hardly could impress anybody with their first names. Does it mean that common for real life names are popular in the cinema world as well? To find out the answer, I took 50 most popular female and male US names for each decade since 1960 and compared their frequency in real life with popularity in movies & TV shows released in the same years.

There's one particularly cunning type of pricing strategy that marketers use to get you to switch your choice from one option to a more expensive or profitable one. It's called the decoy effect. Imagine you are shopping for a Nutribullet blender. You see two options. The cheaper one, at $89, promotes 900 watts of power and a five-piece accessory kit. The more expensive one, at $149, is 1,200 watts and has 12 accessories. Which one you choose will depend on some assessment of their relative value for money. It's not immediately apparent, though, that the more expensive option is better value. It's 50% more powerful but costs almost 80% more. It does have more than twice as many plastic accessories, but what are they worth? Now consider the two in light of a third option. This one, for $125, offers 1,000 watts and nine accessories. It enables you to make what feels like a more considered comparison. For $36 more than the cheaper option, you get four more accessories and an extra 100 watts of power. But if you spend just $24 extra, you get a further three accessories and 200 watts more power. Bargain! You have just experienced the decoy effect.

For decades, researchers and physicians tended to think about pregnancies as either planned or unplanned. But new data reveals that for a significant group of women, their feelings don't neatly fit into one category or another. As many as one-fifth of women who become pregnant aren't sure whether they want a baby. This fact may reshape how doctors and policymakers think about family planning. For women who are unsure, it doesn't seem enough for physicians to counsel them on pregnancy prevention or prenatal care. "In the past we thought of it as binary, you want to be pregnant or not, so you need contraception or a prenatal vitamin," said Maria Isabel Rodriguez, an obstetrician-gynecologist at Oregon Health and Science University whose research focuses on family planning and contraceptive policy. "But it's more of a continuum." The new data comes from a recent change in the Centers for Disease Control and Prevention's big survey of new mothers, now allowing them to answer a question about their pregnancy desires by saying "I wasn't sure." It shows that some women want to avoid making a decision about becoming pregnant, or have strong but mixed feelings about it.

University students, like many in society, demonstrate bias against women and particularly women from non-English speaking backgrounds. That's the take home message from a new and comprehensive analysis of student experience surveys. The study examined a large dataset consisting of more than 500,000 student responses collected over 2010 to 2016. It involved more than 3,000 teachers and 2,000 courses across five faculties at the University of New South Wales (UNSW), Sydney.

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."