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This is an example of imprecise communication - it's right to talk about percentage increase (rather than percentage point change) but it isn't clear what the baseline was which makes it a bit confusing (475,000/120*100 will give you the median price 3 months ago of 395833.33). Note the use of the median rather than the mean - remember why that's a sensible idea in this context! Home prices have risen by more than 20 per cent to a $475,000 median. The change in median price over the past year was up by 26.7 per cent. And compared to three years ago Cremorne prices have grown by almost 40 per cent. Look back five years and prices have increased by a mighty 70 per cent, the report revealed.

A headline in The New York Times today reads "In the Shopping Cart of a Food Stamp Household: Lots of Soda." Is it true? The story itself provides hints that the headline is misleading, and likely to damage the image of the SNAP program and its beneficiaries. This is dangerous, considering that many readers look at clickbaity headlines, like the NYTimes one, but don't read stories. SNAP households aren't different than the rest of households. Most Americans buy and drink way too much soda and, as a result, obesity and Type II diabetes have reached epidemic levels. The story says that households that receive food stamps spend 9.3% of their grocery budget on soft drinks, while families in general spend 7.1%. This is one of those cases when reporting just percentages, and not taking into account other variables, such as total spending in groceries, sounds fishy.

"Significant Digits" is a daily digest of the numbers tucked inside the news by fivethirtyeight.com - e.g. in this issue 29 percent Percentage of Americans who regularly work weekends. Another 27 percent regularly work between the hours of 10 p.m. and 6 a.m. Maybe useful for understanding how important quantitative information is in the world around us.

In this case, because pancreatic cancer is relatively rare, the impressive-sounding 50% reduction doesn't amount to very much. According to the American Cancer Society, a 60-year-old man has a 0.41% (1 in 241) chance of developing pancreatic cancer during the next ten years. (Risk varies greatly with age and is much lower at younger ages.) So cutting that risk in half might bring it down to about 0.2% (1 in 480). It's a 50% drop, sure, but the risk was already very small to begin with. In this case, it's more helpful to news and health care consumers to describe it as a 0.2 percentage point reduction. And then there are the harms of regular aspirin use; the Yale news release that the tweet links to doesn't mention any. But taking aspirin regularly isn't a harmless intervention - far from it. It's well known that taking aspirin every day can cause serious bleeding in the gastrointestinal system and, less frequently, in the brain. That's why guidelines for aspirin use in cardiovascular disease prevention don't recommend it for people at low risk of a heart attack. The potential benefits may be outweighed by the risks of a serious bleed.

As an epidemiologist, I am interested in disease - and more specifically, who in a population currently has or might get that disease. What is their age, sex, or socioeconomic status? Where do they live? What can people do to limit their chances of getting sick? Questions exploring whether something is likely to happen or not can be answered with quantitative research. By counting and measuring, we quantify (measure) a phenomenon in our world, and present the results through percentages and averages. We use statistics to help interpret the significance of the results. While this approach is very important, it can't tell us everything about a disease and peoples' experiences of it. That's where qualitative data becomes important.

A great data-story on the gender imbalance worldwide, using data and research to investigate and highlight key issues. This piece in the economist takes a different approach to using the data https://www.economist.com/node/15636231 "In 1960, the earliest year the World Bank provides data for, the world was within 0.002 percentage points of a perfectly equal distribution. Ever since, the gap has widened; now men outnumber women on the planet by more than 66 million. When this piece was first published in early 2014, the gap had already been the widest ever - the trend continues."

Argument: Because deductions (the amount of income you can claim is not-taxable) will be reduced, even though the tax rate will go down, the richest will not in fact see an absolute reduction in tax paid. Counterargument - looking at the data, this is in fact not the case...

Where else have you seen risk claims like "x causes 50% increase in health-problem y"? Do you tend to trust these claims? Do you understand what they mean? Would they change your behaviour? "Interpreting health (or any other) risk estimates reported in the media is not straightforward. Even health professionals can get tripped up trying to make sense of these statistics, so it is no wonder the public can easily be confused or misled. Often there is tendency to overreact to risk estimates, so it's worth unpacking what these really mean."

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.

Many public conversations we have about science-related issues involve communicating risks: describing them, comparing them and trying to inspire action to avoid or mitigate them. Just think about the ongoing stream of news and commentary on health, alternative energy, food security and climate change. Good risk communication points out where we are doing hazardous things. It helps us better navigate crises. It also allows us to pre-empt and avoid danger and destruction. But poor risk communication does the opposite. It creates confusion, helplessness and, worst of all, pushes us to actively work against each other even when it's against our best interests to do so. So what's happening when risk communications go wrong?

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.

Very cool visualisation, showing the power of thinking in manageable numbers. If Australia were just 100 people, what would it look like? New census data gives us an opportunity to find out, and provides some surprising insights into the state of the nation.

On Friday, the Australian Taxation Office, as it does every April, released the latest batch of annual taxation statistics. And as ever, the data was used in rather contorted ways to suggest the budget needed to reduce the level of taxation paid by the wealthiest and to make it seem like the richest were the ones doing it tough. The other old chestnut that got a run in the Australian is that more school teachers actually use negative gearing than company executives. Again, is it really a shock that "while 72,000 investors were listed as company executives, 99,000 people claiming rental losses on their tax returns were either teachers, nurses or midwives"? Given there are about 300,000 more people working as teachers, nurses or midwives than there are company executives, does anyone really think that because 27,000 more of them might use negative gearing is proof of anything? The crucial thing is not the total number, but the proportion of teachers and nurses (and any other profession) who use negative gearing.

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.