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When talking about the gender pay gap people tend to talk about the median figure rather than the mean. The mean is calculated by adding up all of the wages of employees in a company and dividing that figure by the number of employees. This means the final figure can be skewed by a small number of highly paid individuals. The median is the number that falls in the middle of a range when everyone's wages are lined up from smallest to largest and is more representative when there is a lot of variation in pay.

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.

Use of mean and median to analyse the paygap at The Guardian group. The gender pay gap at Guardian News & Media is 11.3% - calculated by mean hourly pay - the company has reported as part of the government's compulsory gender pay gap initiative.Across GNM, the median pay difference, which takes the mid-point when all wage rates are lined up from the biggest to smallest - which reduces the impact of one-off outliers - is wider, at 12.1%. The figure for non-editorial is 18.2%, and nearly 9% in editorial.

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.

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."

Citizen-generated data is especially important for women's rights issues. In many countries the lack of women in positions of institutional power, combined with slow, bureaucratic systems and a lack of prioritisation of women's rights issues means data isn't gathered on relevant topics, let alone appropriately responded to by the state. Even when data is gathered by institutions, societal pressures may mean it remains inadequate. In the case of gender-based violence, for instance, women often suffer in silence, worrying nobody will believe them or that they will be blamed. Providing a way for women to contribute data anonymously or, if they so choose, with their own details, can be key to documenting violence and understanding the scale of a problem, and thus deciding upon appropriate responses.

Public sector employers with more than 250 staff are legally obliged to publish their gender pay gap by Friday, while private firms and charities have until Wednesday 4 April. About 7,000 of a estimated total of 9,000 organisations had filed results by Thursday.companies have filed mathematically impossible figures - at least 17 have reported a bonus gap of more than 100%. One company reported an hourly mean gender pay gap of 106.4%, implying that for every £100 earned by a man a woman would "pay" £6.40. A spokesperson at the company declined to comment.

The argument is simple, and goes a bit like this. Science does not work by appeal to authority, but rather by the acquisition of experimentally verifiable evidence. Appeals to scientific bodies are appeals to authority, so should be rejected. ...If legitimate authorities are not to be consulted, presumably there is no point in having scientists around at all, as each person would need to verify any claim on their own terms and with their own resources. That would mean a speedy decline into very dark times indeed.

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.

Ocean acidification is an inevitable consequence of increasing carbon dioxide in the atmosphere. That's a matter of fact. We don't know exactly what will happen to complex marine ecosystems when faced with the additional stress of falling pH, but we do know those changes are happening and that they won't be good news.Freedom of speech, and of the press, is, of course, precious. Yet that freedom also brings responsibility. The Editors' Code of Practice - which IPSO says it upholds - requires the "highest professional standards". This includes taking care not to publish inaccurate, misleading or distorted information or images, including headlines not supported by the text. In addition, a significant inaccuracy, misleading statement or distortion must be corrected, promptly and with due prominence, and - where appropriate - an apology published.Is this just an honest opinion, a statement of fact or wilfully misleading and clever rhetoric? That depends on what is meant by "evidence". If it means quality research carried out by scientists with expertise in the field, the statement is factually incorrect. But if evidence includes anything said by non-experts, such as Delingpole, then that's an increase, right?

If you were surprised by the result of the Brexit vote in the UK or by the Trump victory in the US, you might live in an echo chamber - a self-reinforcing world of people who share the same opinions as you. Echo chambers are a problem, and not just because it means some people make incorrect predictions about political events. They threaten our democratic conversation, splitting up the common ground of assumption and fact that is needed for diverse people to talk to each other. A few tools are mentioned in the post that help you see "the other side" of a story - you might like to play with them, e.g. http://politecho.org "is a browser extension that shows the political biases of your friends and Facebook newsfeed"

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.

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.

Whether it's quitting smoking, reducing alcohol intake or making healthier dietary choices, many of us have habits we'd like to change. But it's really hard to know which treatment path to take. To advise their patients on the best of course of action, doctors sometimes compare treatments using something called the "number needed to treat" (NNT). In deciding whether to embark on a course of treatment, NNT can help. But the term is easily misunderstood by patients, and doctors as well. So it's useful to break down what NNT means.

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.

My name is Mary. This is quite a popular girla name in many countries. So, since childhood I've rarely been surprised to meet my namesake in daily life. But movies were a different matter! However, after hundred of watched movies and TV series I assumed that most of the characters had pretty ordinary names as well. James Bond, Jeff Lebowski and Sarah Connor hardly could impress anybody with their first names. Does it mean that common for real life names are popular in the cinema world as well? To find out the answer, I took 50 most popular female and male US names for each decade since 1960 and compared their frequency in real life with popularity in movies & TV shows released in the same years.

For the 11 million Australians with private hospital cover, premium rises are nothing new. The 3.95% average increase on April 1, 2018 will be the seventeenth consecutive year in which insurance premiums have been hiked up. Health insurance premiums have increased by an average of 5.35% per year since 2000, which is significantly more than wage growth, meaning that households are spending a larger share of their income on health care.

The "What's going on in this graph' series is from the NYT Learning Network, and is about interpreting graphs that represent real data to tell a story. It's aimed at high school students but that just means the examples and explanations are a really great introduction to visualising and interpreting data!

Women continue to earn less than men, for a variety of reasons. Discrimination is one, research shows. Women are also likelier than men to work in lower-paying jobs like those in public service, caregiving and the nonprofit sector - and to take time off for children. Employers often base a starting salary on someone's previous one, so at each job, the gender pay gap continues, and it becomes seemingly impossible for women to catch up. Salary history bans are too new for researchers to have studied their effects extensively. But other research has found that people are overly influenced by an opening bid, something social scientists call anchoring bias. This means that if employers learn an applicant's previous salary and it's lower or higher than they were planning to offer, it's likely to influence their offer.

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?