just knowing facts doesn't necessarily guarantee that one's opinions and behaviors will be consistent with them. For example, many people "know" that recycling is beneficial but still throw plastic bottles in the trash. Or they read an online article by a scientist about the necessity of vaccines, but leave comments expressing outrage that doctors are trying to further a pro-vaccine agenda. Convincing people that scientific evidence has merit and should guide behavior may be the greatest science communication challenge, particularly in our "post-truth" era. Luckily, we know a lot about human psychology - how people perceive, reason and learn about the world - and many lessons from psychology can be applied to science communication endeavors.

Not so much about data, but about how we use evidence when we don't know the original source, and how e e.g. a politician failing to deny something might be good confirmation that it's actually true. 5 tips for reading stories with unnamed sources

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.

examples of data journalism in audio / podcast form - including: Right To Remain Silent is one particularly good example, because it's about bad data: specifically. police who manipulated official statistics. You might also listen to Choosing Wrong, which includes a section about polling. Another favourite of mine is an audio story by The Economist about the prostitution industry, based on data scraped from sex trade websites: More bang for your buck (there are even worse puns in the charts). David Rhodes, a BBC data journalist, has a range of stories on his Audioboom account, including pieces on Radio 4, Radio 5 Live, and a piece discussing "Did Greece really not pay 89.5% of their taxes in 2010" from the excellent factchecking radio programme, More or Less.

What is the basis for the argument that poor Americans will be healthier if they are required to pay substantially more for health care? It appears that proponents like Ms. Verma have looked at research and concluded that having Medicaid is often no better than being uninsured - and thus that any private insurance, even with enormous deductibles, must be better. But our examination of research in this field suggests this kind of thinking is based on a classic misunderstanding: confusing correlation for causation.

Washington Post video discussing changes to Medicaid in the US. Republicans say they're not cutting Medicaid, they're increasing funding (just by less), others say this is a cut. The White House includes misleading and incomplete information in its video explaining Obamacare's failures.

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.

Our entire education system is faulty, claim experts. They worry that schools don't prepare kids for the world outside. But how could anyone prove what the future will be like? We set off on a round-the-world sleuthing trip to trace a statistic that has been causing headaches for students, teachers and politicians alike.

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 histogram is one of my favorite basic chart types, because it lets you quickly see the shape and distribution of a dataset. However, a lot of people don't know what a histogram shows or how the chart works.

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/

So what does it mean to be "entitled" to an opinion? If "Everyone's entitled to their opinion" just means no-one has the right to stop people thinking and saying whatever they want, then the statement is true, but fairly trivial. No one can stop you saying that vaccines cause autism, no matter how many times that claim has been disproven. But if 'entitled to an opinion' means 'entitled to have your views treated as serious candidates for the truth' then it's pretty clearly false. And this too is a distinction that tends to get blurred.

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.

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.

"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.

"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)

Data analysis is playing an increasing role in the U.S. electoral system, raising an important question as the Trump administration prepares to oversee the 2020 Census: What if the data aren't reliable?

Nice little interactive piece from the NYTimes - they've charted sets of data from the Bush presidency, can you accurately extend their line charts to show the change over the Obama years?

Mona Chalabi on the perils of polling data and the importance of official statistics