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Alcohol and Other Drug (AOD) Media Watch is based on the same premise as the ABC show Media Watch. It aims to highlight poor examples of journalism regarding AOD-related issues in the hope that we can assist journalists to report more objectively using science and evidence rather than perpetuating myths, opinions and moral panic. Research has found moral panics in the media can actually be detrimental. Moral panics in the media can actually be detrimental by counter-intuitively leading to increased drug use since it increases the perception that more people are using the drug than actually are. It has also been found to found that moral panics reduce the degree to which some people believe that the drug being reported on is harmful. It also reduces the credibility of AOD information in the media.

In 1967, following riots in Detroit, Philip Meyer used survey research methods, powered by a computer, to show that college-educated people were just as likely to have rioted as high school drop outs. His story was one of the first examples of computer assisted reporting and precision journalism, in which journalists use social science methodologies to extract and tell stories. In recognition of his contribution to the area, each year's best computer-driven and precision stories are celebrated through the Philip Meyer Journalism Award. The Award's 2016 winners have just been announced, with the successful entries showcasing techniques derived from quantitative and qualitative methods, such as surveys using randomly-selected respondents, descriptive and inferential statistical analysis, social network analysis, content analysis, field experiments, and more.

It is vital that public policy be driven by rigorous research. In the last decade key policy changes have had profound impacts on nightlife in Sydney's inner city and suburbs. The most significant and controversial of these has been the 2014 "lockout laws".

Machines are besting humans in more and more tasks; thanks to technology, fewer Americans make more stuff in less time. But today economists debate not whether machines are changing the workplace and making us more efficient - they certainly are - but whether the result is a net loss of jobs. The figures above may look dire. But compare the number of manufacturing jobs and total jobs in the chart below. Since 1990, the total non-farm workforce has grown 33 percent, more than accounting for the manufacturing jobs lost.

Numbers are often used to persuade rather than inform, statistical literacy needs to be improved, and so surely we need more statistics courses in schools and universities? Well, yes, but this should not mean more of the same. After years of researching and teaching statistical methods, I am not alone in concluding that the way in which we teach statistics can be counterproductive, with an overemphasis on mathematical foundations through probability theory, long lists of tests and formulae to apply, and toy problems involving, say, calculating the standard deviation of the weights of cod. The American Statistical Association's Guidelines for Assessment and Instruction in Statistics Education (2016) strongly recommended changing the pedagogy of statistics into one based on problemsolving, real-world examples, and with an emphasis on communication.

A BBC episode (30 mins) on changing minds. There's a widespread belief that there's no point talking to people you disagree with because they will never change their minds. Everyone is too polarized and attempts to discuss will merely result in greater polarization. But the history of the world is defined by changes of mind -that's how progress (or even regress) is made: shifts in political, cultural, scientific beliefs and paradigms. So how do we ever change our minds about something? What are the perspectives that foster constructive discussion and what conditions destroy it? Margaret Heffernan talks to international academics at the forefront of research into new forms of democratic discourse, to journalists involved in facilitating national conversations and to members of the public who seized the opportunity to talk to a stranger with opposing political views:

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.

in our research, which involves talking to statisticians, public servants and journalists who produce and communicate the statistics that govern our lives, people say overwhelmingly that faith and trust are essential parts of what makes statistics useful. Despite the objective and impartial appearance of statistics, it is a web of people and human processes that makes them trustworthy.

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.

Mona Chalabi on the perils of polling data and the importance of official statistics