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Podcast from UTS Think:Digital Futures (that I briefly appear on). Sometimes we get caught up thinking data and science are the be all and end all - it can give us a lot of answers sure, but the devil is in the detail. How are we interpreting data wrong? And why do we have trouble communicating it? Presenters/Producers: Cheyne Anderson & Ellen Leabeater Speakers: Jon Wardle - Faculty of Health, University of Technology Sydney Simon Knight - Lecturer, Connected Intelligence Centre, UTS Mark Moritz - Associate Professor, Department of Anthropology, Ohio State University Think: Digital Futures is supported by 2SER and the University of Technology Sydney. http://2ser.com/shows/think-digital-futures/

How we track our economy influences everything from government spending and taxes to home lending and business investment. The Conversation series The Way We Measure takes a close look at economic indicators to better understand what's going on. Ever since 1944, Gross Domestic Product (GDP) has been a primary measure of economic growth. It's in the news regularly and, even though few can define what it means, there is general acceptance that when GDP is growing, things are good. There are problems with this simplistic formulation.

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.

Great podcast from our own 2ser & UTS Business School! "latest episode of #ThinkBusinessFutures @2ser, with Dr Geetanjali Saluja @UTSMarketing @UTS_Business, discussing her research into moral decision making, and Adam Ferrier, author of 'The Advertising Effect: How to Change Behaviour'" Discusses some of the framing, cognitive bias, and their impact on decision making that we talked about in class

'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."

"Between 2000 and 2015, nearly every African country improved childhood nutrition, especially in reducing stunted growth caused by malnutrition" .... " national averages do not tell the full story. In Kenya, for example, rates of wasting in children under 5 were below 6% on average nationwide in 2015, yet in certain regions plagued by several years of poor rains, crop failure and disease outbreaks, estimated levels of wasting reach as high as 28%."... "Such fine-grained insight brings tremendous responsibility to act. It shows governments, international agencies and donors exactly where to direct resources and support."..."This shows how crucial it is to invest in data. Data gaps undermine our ability to target resources, develop policies and track accountability. Without good data, we're flying blind. If you can't see it, you can't solve it."

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.

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.