Group items tagged

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] .

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.

When we launched Beyond the blade earlier this year, we wanted to know how many young people and children were being killed by knives in the UK. Who are these young people being killed?, Where are they dying? Is the scale of the issue changing, and if so how? We spoke to experts about the number of children and teenagers affected in Britain and Northern Ireland. We checked with the Office for National Statistics, the Home Office, politicians, academics and thinktanks. But the answer to how many young people are dying every year, it seemed, was that nobody knows. So we started trying to find out. Until now, there has been no publicly available information about the demographic profiles of those who have died from knife attacks in the UK

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?

So I set out to get to the bottom of the evidence on how we respond to rational arguments. Does rationality lose out every time to irrational motivations? Or is there any hope to those of us who want to persuade because we have good arguments, not because we are handsome, or popular, or offer heavy clipboards.

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.

'Significance' is a magazine published by the UK Royal Statistical Society and American Statistical Association. E.g. this article discusses the data we might use to analyse food security, thinking about what sources of data are available and the questions they might help us answer. "data on food insecurity is biased towards the environment in which it was created and the priorities of those who collect or commission it. Data from schools is concerned with reimbursement; government data might be focused on budgetary constraints or accountability; grocery stores could (if willing) tell us what food is bought, but not how it is used; meanwhile, non-profits are most interested in demonstrating impact to funders. There is a wide variety of data sets available, but very few are created with the intent to understand the real drivers of hunger and poverty. The data may be repurposed, but modellers must be especially careful to moderate the assumptions of each data set. ...... It is also especially important that those experiencing hunger and poverty are consulted when designing any data analysis project. The input of domain experts is crucial to the success of data science endeavours, and those experiencing poverty know the right questions to ask."

There's now a vast network of factcheck units around the world, operating in myriad different languages. However, none have a process quite like ours at The Conversation. We have created an animated 72-second explainer of exactly how our FactCheck process works. It explains how we build in extra checks and balances, such as a blind peer review by a second academic expert and additional checking processes and editorial oversight. We hope you'll share it with others who care about reliable information.

The public understandably focuses on polling results and how much these results seem to vary. Take two presidential approval polls from March 21. Polling firm Rasmussen Reports reported that 50 percent of Americans approve of President Donald Trump's performance, while, that same day, Gallup stated that only 37 percent do. In late February, the website FiveThirtyEight listed 18 other presidential approval polls in which Trump's approval ratings ranged from 39 percent to 55 percent. Some of these pollsters queried likely voters, some registered voters and others adults, regardless of their voting status. Almost half of the polls relied on phone calls, another half on online polling and a few used a mix of the two. Further complicating matters, it's not entirely clear how calling cellphones or landlines affects a poll's results. Each of these choices has a consequence, and the range of results attests to the degree that these choices can influence results.

Journalists often make mistakes when reporting on data such as opinion poll results, federal jobs reports and census surveys because they don't quite understand - or they ignore - the data's margin of error. Data collected from a sample of the population will never perfectly represent the population as a whole. The margin of error, which depends primarily on sample size, is a measure of how precise the estimate is. The margin of error for an opinion poll indicates how close the match is likely to be between the responses of the people in the poll and those of the population as a whole. To help journalists understand margin of error and how to correctly interpret data from polls and surveys, we've put together a list of seven tips, Look for the margin of error - and report it. It tells you and your audience how much the results can vary. Remember that the larger the margin of error, the greater the likelihood the survey estimate will be inaccurate. Make sure a political candidate really has the lead before you report it. Note that there are real trends, and then there are mistaken claims of a trend. Watch your adjectives. (And it might be best to avoid them altogether.) Keep in mind that the margin of error for subgroups of a sample will always be larger than the margin of error for the sample. Use caution when comparing results from different polls and surveys, especially those conducted by different organizations.

A list of non-academic readings related to different aspects of the "fake news" debate, covering the impact of advertising, its role in the US election, the growing awareness of disinformation campaigns aimed at upcoming European elections, and some of the psychological theories that help explain why our brains can be so easily fooled.

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.

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?

An excellent visual data story describing the gender pay gap (in America) and debunking the claim that there is no real difference in the amount men and women get paid. The pay gap varies depending on the occupation, working hours, education attainment, experience, and geography. That explains part of the difference in pay between men and women, but not all of it. And even though most economists agree that after adjusting for age, education, experience and other variables there's still an unexplained gap, there are voices who argue that the gender pay gap is a myth. Pay gap deniers purport that women's choices, rather than discrimination, cause the pay gap between women and men. But those choices are actually consequences of the social forces at play.

The Guardian datablog has a set of visualisations https://www.theguardian.com/technology/data-visualisation and a set of stories focused on the Australian context; useful for exploring how data analysis and visusalisation are used to tell a story.

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

A complex look at an important issue and what data can tell us about it, and how missing data complicates the picture.

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.

Should newspapers be adding confidence intervals to their graphics? Why, she asked, are newspapers like hers hesitant to print confidence intervals, a statistical measure of uncertainty? With the exception of noting sampling error in polling data, newspapers like the Times only show uncertainty when they're forced to - and often to prove the opposite of what point data might show.