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As Omicron surges through the community, getting the right answer from a Rapid Antigen Test (RAT) is not as straightforward as reading one or two lines off the kit. RATs are a convenient diagnostic tool to detect COVID virus fragments in nasal secretions or saliva. They are designed to be self-administered and give an answer in minutes. Detecting infection early is critical to preventing spread and allowing persons at risk of severe disease to get timely access to close monitoring and new life-saving therapies. As governments plan to distribute tens of millions of RAT kits to schools and workplaces in coming weeks to help Australians work and study safely, it is important that we understand how to best use this diagnostic tool to reduce transmission and unnecessary disruptions to our lives and economy.

Today's Examine dives into the maths. We'll look at the best estimates on catching and being injured by COVID-19, the chances of being harmed by the AstraZeneca vaccine, and the other broader risks and benefits. Hopefully, at the end of this, you are armed to make a better decision.

What happens once misinformation is corrected? Is it effective at all? A major problem for social media platforms resides in the difficulty to reduce the spread of misinformation. In response, measures such as the labeling of false content and related articles have been created to correct users' perceptions and accuracy assessment. Although this may seem a clever initiative coming from social media platforms, helping users to understand which information can be trusted, restrictive measures also raise pivotal questions. What happens to those posts which are false, but do not display any tag flagging their untruthfulness? Will we be able to discern them?

Maths quiz. If you take a Covid test that only gives a false positive one time in every 1,000, what's the chance that you've actually got Covid? Surely it's 99.9%, right? No! The correct answer is: you have no idea. You don't have enough information to make the judgment.

Algorithms decide which applications reach human managers' eyes. But they sort out people with unusual work histories or who lack college degrees

Statistics have played an important role both in our understanding of the coronavirus pandemic, and our attempts to fight it. The RSS sets out ten lessons the government can learn, and a series of recommendations for what they should do now, to ensure that the country's data infrastructure is prepared for the next crisis - whatever form it takes.

It's human nature to spot patterns in data. But we should be careful about finding causal links where none may existStories about people getting blood clots soon after taking the Oxford/AstraZeneca vaccine have become a source of anxiety among European leaders. After a report on a death and three hospitalisations in Norway, which found serious blood clotting in adults who had received the vaccine, Ireland has temporarily suspended the jab. Some anxiety about a new vaccine is understandable, and any suspected reactions should be investigated. But in the current circumstances we need to think slow as well as fast, and resist drawing causal links between events where none may exist.

the BBC has uncovered a sombre data gap that may be having a profound effect on good governance for some countries in Africa. Only eight out of more than 50 African countries investigated by the BBC have a compulsory system to register deaths, meaning many lack a complete view of mortality trends. This could be having a far-reaching influence on a number of key policy areas - including resource allocation and understanding the impact of Covid-19.

Electric vehicles are better for the climate than gas-powered cars, but many Americans are still reluctant to buy them. One reason: The larger upfront cost. New data published Thursday shows that despite the higher sticker price, electric cars may actually save drivers money in the long-run. To reach this conclusion, a team at the Massachusetts Institute of Technology calculated both the carbon dioxide emissions and full lifetime cost - including purchase price, maintenance and fuel - for nearly every new car model on the market. They found electric cars were easily more climate friendly than gas-burning ones. Over a lifetime, they were often cheaper, too.

Roughly put, building a machine-learning model involves training it on a large number of examples and then testing it on a bunch of similar examples that it has not yet seen. When the model passes the test, you're done. What the Google researchers point out is that this bar is too low. The training process can produce many different models that all pass the test but-and this is the crucial part-these models will differ in small, arbitrary ways, depending on things like the random values given to the nodes in a neural network before training starts, the way training data is selected or represented, the number of training runs, and so on. These small, often random, differences are typically overlooked if they don't affect how a model does on the test. But it turns out they can lead to huge variation in performance in the real world. In other words, the process used to build most machine-learning models today cannot tell which models will work in the real world and which ones won't.

the use of historical data to train the AI gives 'a leg-up to people from groups who have traditionally been successful and grants fewer opportunities to minorities and women'.

in our research, which involves talking to statisticians, public servants and journalists who produce and communicate the statistics that govern our lives, people say overwhelmingly that faith and trust are essential parts of what makes statistics useful. Despite the objective and impartial appearance of statistics, it is a web of people and human processes that makes them trustworthy.

We learned this week that the Trump campaign may have tried to dissuade millions of Black voters from voting in 2016 through highly targeted online ads. The investigation, by Channel 4, highlighted a still little-understood online advertising technique, microtargeting. This targets ads at people based on the huge amount of data available about them online. Experts say Big Tech needs to be much more transparent about how microtargeting works, to avoid overblown claims but also counter a potential threat to democracy.

Social media algorithms, artificial intelligence, and our own genetics are among the factors influencing us beyond our awareness. This raises an ancient question: do we have control over our own lives? This article is part of The Conversation's series on the science of free will.

Until the first Abbott-Hockey budget in 2014, a statement of budget measures that disproportionately affect women was published at budget time. At times given different names, the first was delivered with the Hawke government's 1984 budget. In its foreword, then Prime Minister Hawke promised that "within the overall economic objectives of the government" important budget decisions would from then on be made "with full knowledge of their impact on women". These women's budget statements shed light on the impact of decisions that might have been thought to have little to do with gender, such as the Hawke government's reduction of tariffs on imports of clothing, textiles and footwear. The statement pointed out that two-thirds of the workers in these industries were women and that without special support for retraining (which was given) they would be disproportionately disadvantaged. Increasingly, and especially during the Rudd and Gillard governments, the statements made visible the economic impact of women's greater responsibility for unpaid care work.

Blame the algorithm. That's become the go-to refrain for why your Instagram feed keeps surfacing the same five people or why YouTube is feeding you questionable "up next" video recommendations. But you should blame the algorithm - those ubiquitous instructions that tell computer programs what to do - for more than messing with your social media feed. Algorithms are behind many mundane, but still consequential, decisions in your life. The code often replaces humans, but that doesn't mean the results are foolproof. An algorithm can be just as flawed as their human creators. These are just some of the ways hidden calculations determine what you do and experience.

Mashable's series Algorithms explores the mysterious lines of code that increasingly control our lives - and our futures. The world in 2020 has been given plenty of reasons to be wary of algorithms. Depending on the result of the U.S. presidential election, it may give us one more. Either way, it's high time we questioned the impact of these high-tech data-driven calculations, which increasingly determine who or what we see (and what we don't) online. The impact of algorithms is starting to scale up to a dizzying degree, and literally billions of people are feeling the ripple effects. This is the year the Social Credit System, an ominous Black Mirror-like "behavior score" run by the Chinese government, is set to officially launch. It may not be quite as bad as you've heard, but it will boost or tighten financial credit and other incentives for the entire population. There's another billion unexamined, unimpeachable algorithms hanging over a billion human lives.

Protests against racial injustice and the COVID-19 pandemic have exposed racial inequalities rife within social and economic systems around the world. Fed up with police brutality and systemic racism against African Americans and other racialised groups, people staged protests against racial injustice in all 50 states across the United States.Apart from these examples, however, there is surprisingly little data or discourse about the impact of the disease on racial and ethnic minorities in the rest of Europe. This silence speaks volumes about Europe's approach to racism.The vast majority of EU member states do not use the concept of race or ethnic origin in data collection, in spite of policies like the European Racial Equality Directive and the Employment Equality Directive which prohibit racial or ethnic discrimination. France outright prohibits it.Without disaggregated data, it is virtually impossible to quantify the extent of discrimination experienced by racial and ethnic groups or the impacts of COVID-19 on their lives.

The data seems to overwhelmingly point to a criminal justice system riven by racial bias. But, remarkably, it could be even more overwhelming than some studies make it seem. That's because of a statistical quirk called "collider bias," a kind of selection bias that means that the crime data that shows racial bias is, itself, biased by racist practices. If you thought crime data showed clear evidence of racism before, understanding how collider bias affects these analyses might make it even clearer.