UTS-AEI

Our entire education system is faulty, claim experts. They worry that schools don't prepare kids for the world outside. But how could anyone prove what the future will be like? We set off on a round-the-world sleuthing trip to trace a statistic that has been causing headaches for students, teachers and politicians alike.

Let's say you have a spreadsheet of thousands of source names with their phone number, email address, home address, and comments/notes. Depending on how large your list is, manually sorting through that list could get tedious and inefficient. Alternatively, you could set up VLOOKUP formulas to have your source's information pop up by simply typing in the source's name as your lookup value[M1] .

The histogram is one of my favorite basic chart types, because it lets you quickly see the shape and distribution of a dataset. However, a lot of people don't know what a histogram shows or how the chart works.

The plan to use the Australian Bureau of Statistics to conduct the federal government's postal plebiscite on marriage reform raises an interesting question: wouldn't it be easier, and just as accurate, to ask the ABS to poll a representative sample of the Australian population rather than everyone?

This week, Hannah Devlin speaks with some of the statistical vigilantes who are scouring datasets to identify cases of fraud and poor scientific practice. These include the consultant anaesthetist John Carlisle, from Torbay Hospital in Devon, who details his role in the Fujii scandal. Hannah also speaks to a PhD student from Tilburg University in the Netherlands, Michèle Nuijten, about software she has helped develop to "spell-check" statistics found in psychology papers. And finally, we hear from the University of Cambridge's Winton professor for the public understanding of risk, David Spiegelhalter, who is also president of the Royal Statistical Society, about the dangers of statistical malpractice.

A series of short articles, with videos and browser addons "investigating algorithmic injustice and the formulas that influence our lives."

Duncan Green (an advisor for Oxfam), writes a great blog on use of evidence in international development and aid. This one (a guest post) is really interesting on how we measure inequality... 'You would think a question like 'Is inequality going up or down?' would be relatively easy to answer, but sadly it is not. At Oxfam we have identified the growing gap between rich and poor and the impact of high inequality as a serious crisis. But how serious is it really?

How should statisticians, journalists and designers reveal uncertainty in graphics for public consumption? Great short (12 page) essay with loads of examples and figures illustrating how we deal with uncertainty, and some of the problems people face when interpreting uncertainty - for example, when you see a map showing the path a hurricane is expected to take, how do you interpret the line? What does it mean?

Nice resource from the UK Chartered Institute of Public Relations on use of statistics in PR and some pitfalls to avoid - Stats matters in the professions!!

These are uncertain times. The US presidential campaign has been divisive, there is disagreement over Brexit, jitters over China's economy and technology is disrupting traditional labour markets. What is the best way to weather all that uncertainty?

A list of non-academic readings related to different aspects of the "fake news" debate, covering the impact of advertising, its role in the US election, the growing awareness of disinformation campaigns aimed at upcoming European elections, and some of the psychological theories that help explain why our brains can be so easily fooled.

It's pretty common nowadays to see the likes of the Reserve Bank of Australia or the Australian Bureau of Statistics issue warnings about the size of Australian household debt. The reason is that the consequences of poor financial decisions often reach far wider than an individual or family. The global financial crisis showed us how rapidly financial contagion can spread - one person's debt is another person's asset, so when the debt is written off so is the asset. However, there has been little improvement in financial literacy in the wake of the financial crisis, the lack of which was one of the underlying causes.

The Supreme Court does not compute. Or at least some of its members would rather not. The justices, the most powerful jurists in the land, seem to have a reluctance - even an allergy - to taking math and statistics seriously. For decades, the court has struggled with quantitative evidence of all kinds in a wide variety of cases. Sometimes justices ignore this evidence. Sometimes they misinterpret it. And sometimes they cast it aside in order to hold on to more traditional legal arguments. (And, yes, sometimes they also listen to the numbers.) Yet the world itself is becoming more computationally driven, and some of those computations will need to be adjudicated before long. Some major artificial intelligence case will likely come across the court's desk in the next decade, for example. By voicing an unwillingness to engage with data-driven empiricism, justices - and thus the court - are at risk of making decisions without fully grappling with the evidence. This problem was on full display earlier this month, when the Supreme Court heard arguments in Gill v. Whitford, a case that will determine the future of partisan gerrymandering - and the contours of American democracy along with it. As my colleague Galen Druke has reported, the case hinges on math: Is there a way to measure a map's partisan bias and to create a standard for when a gerrymandered map infringes on voters' rights?

Have you ever you looked for a product online and then been recommended the exact thing you need to complement it? Or have you been thinking about a particular purchase, only to receive an email with that product on sale? All of this may give you a slightly spooky feeling, but what you're really experiencing is the result of complex algorithms used to predict, and in some cases, even influence your behaviour.

Algorithms are increasingly making decisions that affect ordinary people's lives. One example of this is so-called "algorithmic lending", with some companies claiming to have reduced the time it takes to approve a home loan to mere minutes. But can computers become better judges of financial risk than human bank tellers? Some computer scientists and data analysts certainly think so.

Nice piece on computational thinking and data journalism. For example... This story, published in the UK tabloid newspaper The Mirror, is a great example of understanding how a computer might 'see' information and be able to help you extract a story from it. The data behind the story is a collection of over 300,000 pieces of sheet music. On paper that music would be a collection of ink on paper. But because that has now been digitised, it is now quantified. That means we can perform calculations and comparisons against it. We could: Count the number of notes Calculate the variety (number of different) of notes Identify the most common notes Identify the notes with the maximum value Identify the notes with the minimum value Calculate a 'range' by subtracting the minimum from the maximum The journalist has seen this, and decided that the last option has perhaps the most potential to be newsworthy - we assume some singers have wider ranges than others, and the reality may surprise us (a quality of newsworthiness).

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!

The Media Has A Probability Problem The media's demand for certainty - and its lack of statistical rigor - is a bad match for our complex world.

Data aren't dead, contrary to what some pundits stated post-election [2], rather the limitations of data are not always well reported. While pollsters will be reworking their models following the election, what can media journalists do to improve their overall coverage of statistical issues in the future? First, discuss possible statistical biases, such as errors in sampling and polling, and what impact these might have on the results. Second, always provide measures of uncertainty, and root these uncertainties in real-world examples.