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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?

At The Economist, we take data visualisation seriously. Every week we publish around 40 charts across print, the website and our apps. With every single one, we try our best to visualise the numbers accurately and in a way that best supports the story. But sometimes we get it wrong. We can do better in future if we learn from our mistakes - and other people may be able to learn from them, too. After a deep dive into our archive, I found several instructive examples. I grouped our crimes against data visualisation into three categories: charts that are (1) misleading, (2) confusing and (3) failing to make a point. For each, I suggest an improved version that requires a similar amount of space - an important consideration when drawing charts to be published in print.

Australian farms generate huge volumes of agricultural data. Examples include the types of crops being grown, crop yields, livestock numbers and locations, types of fertilisers and pesticides being used, soil types, rainfall and more. This data is typically collected through the use of digital farming machinery and buildings featuring robotics and digital technologies, artificial intelligence, and devices connected to the internet ("internet of things", or IoT). But a recent review from the Australian Bureau of Statistics and the Australian Bureau of Agricultural and Resource Economics highlights the patchy and fragmented nature of existing government and industry approaches to agricultural data. What that means is Australian farmers are currently not adequately protected from their farm data being collected and used without their knowledge or consent.

Any great story means visualization and detail. It takes the small additions of those details to build a picture in someone's mind to truly make the story complete. The same goes for analytics and data. Data is just a collection of numbers until you turn it into a story. Showing reports and dashboards can be overwhelming without adding a narrative to the data. Any great insight explains what happened, why it is important and how you can use it to turn it into something actionable. Data visualization is using data and statistics in creative ways to show patterns and draw conclusions about a hypothesis, or prove theories, that can help drive decisions in the organization.

If you look at marathon times, you see most people run somewhere in the middle (4 hoursish), with a few under 3 hours (?!) and over 6 hours (very sensible). If you plot this data in a histogram, you can also see that there are spikes...people run just under 3, 3.5, 4, and 4.5 hours - you can see that people have a goal (of a nice round number) and the times are distributed accordingly.

In our teens and early twenties, we're still figuring out what we want to be when we grow up. As we get older, we start to settle into a career. In between, we switch jobs in the search. Based on data from the Current Population Survey, this is when people make the switches and the jobs they switch to.The chart above shows the rate by age, relative to the total number of people who switched to each job. So you see a lot of switching in the early years, and then things seem to settle down at older ages. If someone takes a new job when they're older, it tends towards management or jobs that require more education.

Mona Chalabi (@MonaChalabi) is the Data Editor of The Guardian US and a columnist at New York Magazine. Mona uses hand-drawn visualisations and data stories to make the numbers more relatable. Before getting into journalism, Mona worked in the nonprofit sector, first at the Bank of England, then Transparency International and the International Organization for Migration. 30 minute podcast interview, with some great examples of why data matters, and how to tell stories with data.

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 the importance of having data, in order to understand and tackle an issue... The most recent U.S. estimate...concluded approximately 513,000 women and girls at risk of genital mutilation...But that number should be taken with a big grain of salt...the data doesn't account for immigrants from countries where female genital cutting isn't studied or widely practiced...."You also can't assume that people who come to the U.S. are a representative sample of their country of origin," Clark said. That's especially problematic for estimating rates of female genital cutting, since it's not practiced uniformly within countries. It's also possible, he said, that some immigrants abandon the procedure as they assimilate....some advocates point out that although the estimates focus on immigrants, ...female genital cutting isn't new to the U.S. Female circumcision was performed as a treatment for masturbation by American physicians as recently as the mid-20th century...

Nice article, don't just show the data, tell us what it means "I use a Misfit activity tracker to count my steps. The Misfit app does a decent job of showing me step counts per day and every month, misfit also sends me a summary of the previous month's activity. Unfortunately, the numbers in that summary are presented without any context, making that summary almost entirely useless."

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.

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.

"Fitness trackers out of step when measuring calories, research shows," The Guardian reports. An independent analysis of a number of leading brands found they were all prone to inaccurate recording of energy expenditure.

Say you have a 3,000-person presidential election survey from a state where 3 percent of the population is black. If your survey is exactly representative of reality, you'd end up with 90 black people out of that 3,000. Then you ask them who they plan to vote for (for our purposes, we're assuming they're all voting). History suggests the vast majority will go with the Democrat. Over the last five presidential elections, Republicans have earned an average of only 7 percent of the black vote nationwide. However, your survey comes back with 19.5 percent of black voters leaning Republican. Now, that's the sort of unexpected result that's likely to draw the attention of a social scientist (or a curious journalist). But it should also make them suspicious. That's because when you're focusing on a tiny population like the black voters of a state with few black citizens, even a measurement error rate of 1 percent can produce an outcome that's wildly different from reality. That error could come from white voters who clicked the wrong box and misidentified their race. It could come from black voters who meant to say they were voting Democratic. In any event, the combination of an imbalanced sample ratio and measurement error can be deadly to attempts at deriving meaning from numbers - a grand piano dangling from a rope above a crenulated, four-tiered wedding cake. Just a handful of miscategorized people and - crash! - your beautiful, fascinating insight collapses into a messy disaster.

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.

"Meditation can leave you feeling even more stressed," the Daily Mail reports. The claim is prompted by a study of 60 practitioners of Buddhist meditation in the US which found they'd had a range of "challenging or difficult" experiences associated with the practice. However, it's not clear how relevant the results are to the majority of people who use meditation apps or take mindfulness classes. This article discusses a great example of bad medical reporting from the daily mail. Note how the numbers stack up (e.g "almost 100" is actually 60 patients)

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.

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.

Let's say you have a spreadsheet of thousands of source names with their phone number, email address, home address, and comments/notes. Depending on how large your list is, manually sorting through that list could get tedious and inefficient. Alternatively, you could set up VLOOKUP formulas to have your source's information pop up by simply typing in the source's name as your lookup value[M1] .

The Supreme Court does not compute. Or at least some of its members would rather not. The justices, the most powerful jurists in the land, seem to have a reluctance - even an allergy - to taking math and statistics seriously. For decades, the court has struggled with quantitative evidence of all kinds in a wide variety of cases. Sometimes justices ignore this evidence. Sometimes they misinterpret it. And sometimes they cast it aside in order to hold on to more traditional legal arguments. (And, yes, sometimes they also listen to the numbers.) Yet the world itself is becoming more computationally driven, and some of those computations will need to be adjudicated before long. Some major artificial intelligence case will likely come across the court's desk in the next decade, for example. By voicing an unwillingness to engage with data-driven empiricism, justices - and thus the court - are at risk of making decisions without fully grappling with the evidence. This problem was on full display earlier this month, when the Supreme Court heard arguments in Gill v. Whitford, a case that will determine the future of partisan gerrymandering - and the contours of American democracy along with it. As my colleague Galen Druke has reported, the case hinges on math: Is there a way to measure a map's partisan bias and to create a standard for when a gerrymandered map infringes on voters' rights?