Group items tagged

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.

Once your business has started collecting and combining all kinds of data, the next elusive step is to extract value from it. Your data may hold tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes. During a 2009 interview, Google's Chief Economist Dr. Hal R.Varian stated, "The ability to take data-to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it-that's going to be a hugely important skill in the next decades." Fast forward to 2016 and many businesses would agree with Varian's astute assessment.

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.

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.

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.

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.

In 1967, following riots in Detroit, Philip Meyer used survey research methods, powered by a computer, to show that college-educated people were just as likely to have rioted as high school drop outs. His story was one of the first examples of computer assisted reporting and precision journalism, in which journalists use social science methodologies to extract and tell stories. In recognition of his contribution to the area, each year's best computer-driven and precision stories are celebrated through the Philip Meyer Journalism Award. The Award's 2016 winners have just been announced, with the successful entries showcasing techniques derived from quantitative and qualitative methods, such as surveys using randomly-selected respondents, descriptive and inferential statistical analysis, social network analysis, content analysis, field experiments, and more.

Every year in Australia, the National Assessment Program - Literacy and Numeracy (NAPLAN) results show Indigenous school students are well behind their non-Indigenous peers. Reducing this disparity is a vital part of Australia's national Closing the Gap policy. ... Using an updated version of our equivalent year levels metric, introduced in Grattan Institute's 2016 report Widening Gaps, we estimate year nine Indigenous students in very remote areas are: five years behind in numeracy six years behind in reading, and seven to eight years behind in writing. In other words, the average year nine Indigenous student in a very remote area scores about the same in NAPLAN reading as the average year three non-Indigenous city student, and significantly lower in writing. But it would be a big mistake to see this only as a problem for isolated outback communities. Most Indigenous students live in cities or regional areas. So, even though learning outcomes are worse in remote and very remote areas, city and regional students account for more than two-thirds of the lost years of learning.

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."

BBC guidelines on reporting statistics - excellent resource for AEI! http://downloads.bbc.co.uk/rmhttp/guidelines/editorialguidelines/pdfs/ReportingStatistics.pdf The BBC "accepts recommendations [...] to improve statistical training for BBC journalists and to ensure that journalists are better placed to challenge statistical claims made by people in public office...." It also has "plans to create a 'hub' for data journalism, recruit a new head of statistics and develop guidance based on 'guidelines from, for example, the Royal Statistical Society and others'."

We all do it in journalism. We are taught to write a headline that a) captures what the story is about, and b) captures the reader's attention. Nothing wrong with that. Where the problem comes in is if the headline misleads or misinforms. And, as is so often the case with healthcare topics, that sort of disconnect has the potential to do more harm than good.

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.

Alcohol and Other Drug (AOD) Media Watch is based on the same premise as the ABC show Media Watch. It aims to highlight poor examples of journalism regarding AOD-related issues in the hope that we can assist journalists to report more objectively using science and evidence rather than perpetuating myths, opinions and moral panic. Research has found moral panics in the media can actually be detrimental. Moral panics in the media can actually be detrimental by counter-intuitively leading to increased drug use since it increases the perception that more people are using the drug than actually are. It has also been found to found that moral panics reduce the degree to which some people believe that the drug being reported on is harmful. It also reduces the credibility of AOD information in the media.

'Killer facts' are those punchy, memorable, headline-grabbing statistics that make reports special. They cut through the technicalities to fire people up about changing the world. They are picked up and repeated endlessly by the media and politicians. They are known as 'killer' facts because if they are really effective, they 'kill off' the opposition's arguments. The right killer fact can have more impact than the whole of a well-researched report.

The quest for balance in a health news story can fail before the first sentence if the headline isn't appropriately calibrated. With that in mind, I looked at news stories and releases that we reviewed over the past month and compared the headline message with that of the study on which the news is based. About a third of news story headlines and a quarter of news release headlines either misstated the results or went beyond what the research could support.