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Christopher Pyne has overstated how well Australia is performing on jobs and growth compared to other major economies. IMF estimates for 2016 on GDP growth had put Australia ahead of the G7 countries. But the latest available data - which are actual figures as of the third quarter of 2016, not estimates - show that Australia's cumulative growth in 2016 so far is at the level of the G7 and not higher. So Australia is performing in line with the G7 and slightly worse than the OECD average.

Roughly put, building a machine-learning model involves training it on a large number of examples and then testing it on a bunch of similar examples that it has not yet seen. When the model passes the test, you're done. What the Google researchers point out is that this bar is too low. The training process can produce many different models that all pass the test but-and this is the crucial part-these models will differ in small, arbitrary ways, depending on things like the random values given to the nodes in a neural network before training starts, the way training data is selected or represented, the number of training runs, and so on. These small, often random, differences are typically overlooked if they don't affect how a model does on the test. But it turns out they can lead to huge variation in performance in the real world. In other words, the process used to build most machine-learning models today cannot tell which models will work in the real world and which ones won't.

Lots of quantitative information in this video about the impact of sugar on health in the US. Using comedy/performance to make a point about statistics regarding a social/health issue.

John Oliver explores America's wage gap between men and women and proposes a possible solution. Note: Solution proposed is 100% sarcastic. Think about the arguments being made and how they shift - e.g. the claim that 4% is "basically no wage gap" as a way to indicate a gap isn't in fact a problem. Using comedy/performance to make a point about statistics regarding a social issue

Last Week Tonight clip: John Oliver hosts a mathematically representative climate change debate, with the help of special guest Bill Nye the Science Guy, of course. Using performence & comedy to communicate a scientific point

Using comedy/performance to make a point about statistics regarding a social/health issue.

Using comedy/performance to make a point about statistics regarding a social/health issue.

Going beyond a surface level analysis of the statistics to delve deeper into the story A similar contrast can be seen in the census data on the religiosity of Australians. The census asks participants to state their religion. The answers reveal that while on the one hand Australians are becoming less religious, on the other they are becoming more religiously diverse. In the 2011 census, 68.3% of people identified themselves as having a religion. This was down from 69.5% in 2006. However, the census does not tell the whole story. It cannot tell us how often a person attends a church, mosque, synagogue or temple. It cannot tell us how often a person prays or performs some other religious ritual.

The public understandably focuses on polling results and how much these results seem to vary. Take two presidential approval polls from March 21. Polling firm Rasmussen Reports reported that 50 percent of Americans approve of President Donald Trump's performance, while, that same day, Gallup stated that only 37 percent do. In late February, the website FiveThirtyEight listed 18 other presidential approval polls in which Trump's approval ratings ranged from 39 percent to 55 percent. Some of these pollsters queried likely voters, some registered voters and others adults, regardless of their voting status. Almost half of the polls relied on phone calls, another half on online polling and a few used a mix of the two. Further complicating matters, it's not entirely clear how calling cellphones or landlines affects a poll's results. Each of these choices has a consequence, and the range of results attests to the degree that these choices can influence results.

On the importance of having data, in order to understand and tackle an issue... The most recent U.S. estimate...concluded approximately 513,000 women and girls at risk of genital mutilation...But that number should be taken with a big grain of salt...the data doesn't account for immigrants from countries where female genital cutting isn't studied or widely practiced...."You also can't assume that people who come to the U.S. are a representative sample of their country of origin," Clark said. That's especially problematic for estimating rates of female genital cutting, since it's not practiced uniformly within countries. It's also possible, he said, that some immigrants abandon the procedure as they assimilate....some advocates point out that although the estimates focus on immigrants, ...female genital cutting isn't new to the U.S. Female circumcision was performed as a treatment for masturbation by American physicians as recently as the mid-20th century...

Everyone makes claims about what works. Politicians claim that stop-and-search policing will reduce violent crime; friends might assert that vaccines cause autism; advertisers declare that natural food is healthy. A group of scientists describes giving all schoolchildren deworming pills in some areas as one of the most potent anti-poverty interventions of our time. Another group counters that it does not improve children's health or performance at school. Unfortunately, people often fail to think critically about the trustworthiness of claims, including policymakers who weigh up those made by scientists. Schools do not do enough to prepare young people to think critically1. So many people struggle to assess evidence. As a consequence, they might make poor choices. To address this deficit, we present here a set of principles for assessing the trustworthiness of claims about what works, and for making informed choices (see 'Key Concepts for Informed Choices'). We hope that scientists and professionals in all fields will evaluate, use and comment on it.

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).

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.