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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.

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.

Data aren't dead, contrary to what some pundits stated post-election [2], rather the limitations of data are not always well reported. While pollsters will be reworking their models following the election, what can media journalists do to improve their overall coverage of statistical issues in the future? First, discuss possible statistical biases, such as errors in sampling and polling, and what impact these might have on the results. Second, always provide measures of uncertainty, and root these uncertainties in real-world examples.

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.

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?

The Media Has A Probability Problem The media's demand for certainty - and its lack of statistical rigor - is a bad match for our complex world.