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Why probability and understanding statistical fallacies matters Some of our most important decisions are shaped by something as random as the order in which we make them. The gambler's fallacy, as it's known, affects loan officers, federal judges -- and probably you too. How to avoid it? The first step is to admit just how fallible we all are.

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.

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.

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.

To communicate uncertainty in climate change models and predictions, the IPCC uses a range of expressions to describe the probability that a particular event will occur. For example, in the phrase: "It is very likely that heat extremes will become more frequent in the future," the phrase 'very likely' is used to describe a likelihood of more than 90 per cent, says Smithson.

Medicine's decades-long march toward patient autonomy means patients are often now asked to make the hard decisions - to weigh trade-offs, to grapple with how their values suggest one path over another. This is particularly true when medical science doesn't offer a clear answer: Doctors encourage patients to decide where evidence is weak, while making strong recommendations when evidence is robust. But should we be doing the opposite?People in general are not great at evaluating risk. They worry more about shark attacks than car crashes.

journalists often write about public opinion polls, which are designed to measure the public's attitudes about an issue or idea. Some of the most high-profile polls center on elections and politics. Newsrooms tend to follow these polls closely to see which candidates are ahead, who's most likely to win and what issues voters feel most strongly about. Other polls also offer insights into how people think. For example, a government agency might commission a poll to get a sense of whether local voters would support a sales tax increase to help fund school construction. Researchers frequently conduct national polls to better understand how Americans feel about public policy topics such as gun control, immigration reform and decriminalizing drug use. When covering polls, it's important for journalists to try to gauge the quality of a poll and make sure claims made about the results actually match the data collected. Sometimes, pollsters overgeneralize or exaggerate their findings. Sometimes, flaws in the way they choose participants or collect data make it tough to tell what the results really mean. Below are 11 questions we suggest journalists ask before reporting on poll results. While most of this information probably won't make it into a story or broadcast, the answers will help journalists decide how to frame a poll's findings - or whether to cover them at all.

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.

Interesting discussion of how we perceive risk, and the risks of everyday activities! So let's answer the first question: how likely is a fatal shark attack for an Australian? To get a crude estimate of this, averaged across the whole population, you would divide the number of people who have died due to a shark attack each year (on average three to four each year based on recent data) by the population of Australia (approximately 24 million). This yields a risk of approximately one in eight million per year, which is thankfully very low. Does this assuage your fear? If not, the reason is probably that the imagery of a shark attack is so terrifying. Any unusual and dramatic event has a huge impact on our psyche and this distorts our perception. Also, it's not that easy for us to interpret what a risk expressed as a relative frequency truly means.

How excited would you be about a medication that lowered your risk of cardiovascular death, heart attack or stroke by 1.5%? Excited enough to spend a few thousand dollars a year on the drug? I expect not. What if, instead, the drug reduced those same terrible outcomes by 20%? That's probably enough benefit to interest some in the drug. Well, those statistics come from the same clinical trial, evaluating the same drug. In fact, they present the exact same results, but they simply do it in different ways.

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.

Adrienne is a Marvel movie fanatic: Her favorite films all involve the Hulk, Thor or Black Panther. Brandon prefers animated features like Inside Out, The Incredibles and anything with Buzz Lightyear. I like both kinds, although I'm probably closer to Adrienne than Brandon. And I might skew a little toward Cora, who loves thrillers like Get Out and The Shining. Whose movie preferences are closest to yours: Adrienne's, Brandon's or Cora's? And how far are your cinematic tastes from those of the other two? It might seem strange to ask "how far" here. That's a question about distance, after all. What does distance mean when it comes to which movies you like? How would we measure it?

Interesting discussion of evidence on what does, and does not, work in tackling gender bias in recruitment and management processes. Evidence shows that skills-based assessment tasks (where candidates are given tests that replicate the work they'll actually do on the job) and structured interviews (where all candidates are given the same questions in the same order) have a positive impact on diverse recruitment. Unstructured interviews are more likely to allow unfair bias to creep in. Making promotion and pay processes more transparent can reduce pay inequality: when decisions are reviewed by others, managers realise they need to be objective and evidence-based. Evidence also shows women ask for less money than men. To encourage them to negotiate more, employers should make the possible salary range for roles clear. Studies indicate that women are put off negotiating when they're not sure what a reasonable offer is. "A lot of employers are genuinely really keen to reduce the gender pay gap, and also want to show they're making a change. But they're starved for information about what is likely to work,"

You may have heard the advice for pregnant women to avoid eating soft cheeses. This is because soft cheeses can sometimes carry the Listeria monocytogenes bacteria, which can cause a mild infection. In some cases, the infection can be serious, even fatal, for the unborn child. However, the infection is very rare, affecting only around 65 people out of 23.5 million in Australia in 2014. That's 0.0003% of the population. Of these, only around 10% are pregnant women. Of these, only 20% of infections prove fatal to the foetus. We're getting down to some very small numbers here. If we talked about every risk factor in our lives the way health authorities talk about soft cheeses, we'd likely don a helmet and kneepads every morning after we get out of bed. And we'd certainly never drive a car. The upshot of this example is to emphasise that our intuitions about risk are often out of step with the actualities. So journalists need to take great care when reporting risk so as not to exacerbate our intuitive deficits as a species.

McDonald's Monopoly competition is back this month offering a chance to win expensive prizes, all for the price of a Big Mac. Given you could become tens of thousands of dollars richer by simply going on a Macca's run, McDonald's Monopoly games have in the past been subject to cheating and a multimillion-dollar scandal. But for those who prefer to play fair, what are your chances of actually snaring a prize?

People use imprecise words to describe the chance of events all the time - "It's likely to rain," or "There's a real possibility they'll launch before us," or "It's doubtful the nurses will strike." Not only are such probabilistic terms subjective, but they also can have widely different interpretations. One person's "pretty likely" is another's "far from certain." Our research shows just how broad these gaps in understanding can be and the types of problems that can flow from these differences in interpretation.

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?

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?

Maths quiz. If you take a Covid test that only gives a false positive one time in every 1,000, what's the chance that you've actually got Covid? Surely it's 99.9%, right? No! The correct answer is: you have no idea. You don't have enough information to make the judgment.