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During the 2016 EU referendum campaign, both sides used statistics pretty freely to back their arguments. Understandably, UK broadcasters felt compelled to balance competing perspectives, giving audiences the opportunity to hear the relative merits of leaving or remaining in the EU. In doing so, however, the truth of these statistical claims was not always properly tested. This might help explain some of the public's misconceptions about EU membership. So, for example, although independent sources repeatedly challenged the Leave campaign's claim that the UK government spent £350m per week on EU membership, an IPSOS MORI survey found that almost half of respondents believed this was true just days before the election. Of the 6,916 news items examined in our research, more than 20% featured a statistic. Most of these statistical references were fairly vague, with little or limited context or explanation. Overall, only a third provided some context or made use of comparative data. Statistics featured mostly in stories about business, the economy, politics and health. So, for example, three-quarters of all economics items featured at least one statistic, compared to almost half of news about business. But there were some areas - where statistics might play a useful role in communicating trends or levels of risk - that statistics were rarely used.

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.

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.

BBC guidelines on reporting statistics - excellent resource for AEI! http://downloads.bbc.co.uk/rmhttp/guidelines/editorialguidelines/pdfs/ReportingStatistics.pdf The BBC "accepts recommendations [...] to improve statistical training for BBC journalists and to ensure that journalists are better placed to challenge statistical claims made by people in public office...." It also has "plans to create a 'hub' for data journalism, recruit a new head of statistics and develop guidance based on 'guidelines from, for example, the Royal Statistical Society and others'."

Journalists know they need to get better with data and statistics, but they have a long way to go Only 25 percent of journalists surveyed said they were "very" well equipped to interpret statistics from sources, and only 11 percent said the same about doing statistical analysis themselves.

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.

By UTS' Stephen Woodcock. Statistics is a useful tool for understanding the patterns in the world around us. But our intuition often lets us down when it comes to interpreting those patterns. In this series we look at some of the common mistakes we make and how to avoid them when thinking about statistics, probability and risk.

Not a day goes by without a flurry of data-driven memes passing through my Facebook news feed, sailing by on my Twitter stream or landing as alerts in my email inbox that cite what appear to be reputable datasets and using them to offer surprising conclusions, typically wrapped up in a mesmerizing infographic. Yet, when I pick any of these memes at random and delve into it, I find that it is the rare meme indeed that stands up to statistical scrutiny.

Statistics is a useful tool for understanding the patterns in the world around us. But our intuition often lets us down when it comes to interpreting those patterns. In this series we look at some of the common mistakes we make and how to avoid them when thinking about statistics, probability and risk.

Statistics have played an important role both in our understanding of the coronavirus pandemic, and our attempts to fight it. The RSS sets out ten lessons the government can learn, and a series of recommendations for what they should do now, to ensure that the country's data infrastructure is prepared for the next crisis - whatever form it takes.

'Significance' is a magazine published by the UK Royal Statistical Society and American Statistical Association. E.g. this article discusses the data we might use to analyse food security, thinking about what sources of data are available and the questions they might help us answer. "data on food insecurity is biased towards the environment in which it was created and the priorities of those who collect or commission it. Data from schools is concerned with reimbursement; government data might be focused on budgetary constraints or accountability; grocery stores could (if willing) tell us what food is bought, but not how it is used; meanwhile, non-profits are most interested in demonstrating impact to funders. There is a wide variety of data sets available, but very few are created with the intent to understand the real drivers of hunger and poverty. The data may be repurposed, but modellers must be especially careful to moderate the assumptions of each data set. ...... It is also especially important that those experiencing hunger and poverty are consulted when designing any data analysis project. The input of domain experts is crucial to the success of data science endeavours, and those experiencing poverty know the right questions to ask."

Long read from the New Yorker on employment statistics. Good economic statistics benefit the left and the right, government and business. Without reliable data, businesses can't take risks on investments. Boeing, for example, decides how many 787 Dreamliners to build and therefore how many people to employ based on its Current Market Outlook forecast, which is rooted in government data and projects aircraft demand for the next twenty years.

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!!

Excellent analysis from the FT (you'll need to login to view via the link) that uses knowledge of the Mean and Median to show that some companies have reported incorrect (fabricated?) pay-gap information! One in 20 UK companies that have submitted gender pay gap data to the government have reported numbers that are statistically improbable and therefore almost certainly inaccurate, a Financial Times analysis has found. Sixteen companies, each with more than 250 employees, reported that they paid their male and female staff exactly the same, that is they had a zero average gender pay gap measured by both the mean and median. Experts on pay said that it was highly anomalous for companies of that size to have median and mean pay gaps that were identical because the two statistics measure different things. The mean gap measures the difference between the average male and female salary while the median gap is calculated using the midpoint salary for each gender.

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.

Of the 6,916 news items examined in our research, more than 20% featured a statistic. Most of these statistical references were fairly vague, with little or limited context or explanation. Overall, only a third provided some context or made use of comparative data.

An introduction to a set of deep dive articles an important issue....When we tell people that we work at the intersection of statistics and human rights, the reaction is often surprise. Everyone knows that lawyers and journalists think about human rights problems … but statisticians? Yet, documenting and proving human rights abuses frequently involves the need for quantification. In the case of war crimes and genocide, guilt or innocence can hinge on questions of whether violence was systematic and widespread or one group was targeted at a differential rate compared to others. Similar issues can arise in assessing violations of civil, social, and economic rights. Sometimes the questions can be answered through simple tabulations, but often, more-complex methods of data collection and analysis are required.

The conceptual, practical, and ethical issues surrounding "big data" and data in general begin at the very moment of data collection. Particularly when the data concern people, not enough attention is paid to the realities entangled within that significant moment and spreading out from it.1. Data sets are the results of their means of collection. It's easy to forget that the people collecting a data set, and how they choose to do it, directly determines the data set. An illustrative example can be found in the statistics for how many hate crimes were committed in the United States in 2012. According to the FBI Uniform Crime Reporting Program (UCR), the number was 5,796. However, the Department of Justice's Bureau of Statistics reported 293,800 hate crimes.

Statistics is a useful tool for understanding the patterns in the world around us. But our intuition often lets us down when it comes to interpreting those patterns. In this series we look at some of the common mistakes we make and how to avoid them when thinking about statistics, probability and risk.

Sometimes it's hard to know what statistics are worthy of trust. But we shouldn't count out stats altogether ... instead, we should learn to look behind them. In this delightful, hilarious talk, data journalist Mona Chalabi shares handy tips to help question, interpret and truly understand what the numbers are saying.