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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.

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.

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.

The plan to use the Australian Bureau of Statistics to conduct the federal government's postal plebiscite on marriage reform raises an interesting question: wouldn't it be easier, and just as accurate, to ask the ABS to poll a representative sample of the Australian population rather than everyone?

For decades, researchers and physicians tended to think about pregnancies as either planned or unplanned. But new data reveals that for a significant group of women, their feelings don't neatly fit into one category or another. As many as one-fifth of women who become pregnant aren't sure whether they want a baby. This fact may reshape how doctors and policymakers think about family planning. For women who are unsure, it doesn't seem enough for physicians to counsel them on pregnancy prevention or prenatal care. "In the past we thought of it as binary, you want to be pregnant or not, so you need contraception or a prenatal vitamin," said Maria Isabel Rodriguez, an obstetrician-gynecologist at Oregon Health and Science University whose research focuses on family planning and contraceptive policy. "But it's more of a continuum." The new data comes from a recent change in the Centers for Disease Control and Prevention's big survey of new mothers, now allowing them to answer a question about their pregnancy desires by saying "I wasn't sure." It shows that some women want to avoid making a decision about becoming pregnant, or have strong but mixed feelings about it.

During the 2016 EU referendum campaign, both sides used statistics pretty freely to back their arguments. Understandably, UK broadcasters felt compelled to balance competing perspectives, giving audiences the opportunity to hear the relative merits of leaving or remaining in the EU. In doing so, however, the truth of these statistical claims was not always properly tested. This might help explain some of the public's misconceptions about EU membership. So, for example, although independent sources repeatedly challenged the Leave campaign's claim that the UK government spent £350m per week on EU membership, an IPSOS MORI survey found that almost half of respondents believed this was true just days before the election. 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. Statistics featured mostly in stories about business, the economy, politics and health. So, for example, three-quarters of all economics items featured at least one statistic, compared to almost half of news about business. But there were some areas - where statistics might play a useful role in communicating trends or levels of risk - that statistics were rarely used.

Interesting discussion of wealth vs income inequality, and our perceptions of wealth inequality and how to tackle it. The Essential Report survey highlights the lack of awareness most of us have about the level of wealth inequality that exists, and also our confusion over what policies are best suited to dealing with it.

University students, like many in society, demonstrate bias against women and particularly women from non-English speaking backgrounds. That's the take home message from a new and comprehensive analysis of student experience surveys. The study examined a large dataset consisting of more than 500,000 student responses collected over 2010 to 2016. It involved more than 3,000 teachers and 2,000 courses across five faculties at the University of New South Wales (UNSW), Sydney.

Since the census is the ultimate measure of population in the U.S., one might wonder how we could even know if its count was off. In other words, who recounts the count? Well, the Census Bureau itself, but using a different data source. After each modern census, the bureau carries out research to gauge the accuracy of the most recent count and to improve the survey for the next time around. The best method for determining the scope of the undercount is refreshingly simple: The bureau compares the total number of recorded births and deaths for people of each birth year, then adds in an estimate of net international migration and … that's it. With that number, the bureau can vet the census - which missed 4.6 percent of kids under 5 in 2010, according to this check.

Advancement toward gender equality at work has slowed since the 1990s for three major reasons: people's attitudes stopped becoming more gender egalitarian, occupations stopped gender integrating, and the gender wage gap began decreasing at slower rates. Sociologist Paula England has called this phenomenon an "uneven and stalled" gender revolution, and there have been dozens of studies showing how the progress in gender equality experienced during and immediately after the feminist movement of the 1970s has not been sustained through the 1990s and 2000s. Does this stalled revolution play out in management positions, too? And if so, how? To explore this, I used data on full-time managers obtained from the U.S. Census and American Community Survey for the years 1980 and 2010 to examine three major factors that contribute to gender equality in the labor force: women's representation in management, the occupational gender segregation among managers, and the gender wage gaps that vary across managerial occupations.

Journalists know they need to get better with data and statistics, but they have a long way to go Only 25 percent of journalists surveyed said they were "very" well equipped to interpret statistics from sources, and only 11 percent said the same about doing statistical analysis themselves.

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.

Cool study showing the potential of data analyses to give us new kinds of insights! What does instagram tell us about peoples' nutrition decisions? Food deserts, or places where people have limited access to fresh food, are usually measured by the distance people have to travel to get to a large grocery store. What's harder to measure is what the residents of these areas are actually eating day to day. To do so, researchers typically have to rely on surveys, ...In a recent study, De Choudhury and her colleagues propose another method: mining Instagram. All those artfully arranged plates, all that latte art, just waiting for someone to analyze it!

Excellent NYT interactive - where do you draw the line on how your data is used by social media companies and smart devices?

When it comes to inequality, a growing body of evidence shows that people across countries underestimate the size of the gap between the rich and poor, including their wages. This can undermine support for policies to tackle inequality and even lead to apathy that consolidates the gap. But how exactly are existing perceptions of inequality measured by social scientists?