Although both the law and statistical theory have foundations that rest on formal rules and principles, courts can badly misapply statistical evidence and arguments. In some cases, even when arriving at a correct decision, the courts can accept or give an explanation that is inaccurate and unsound. In other cases, the misuse of statistics has led to false convictions and years of jail time for crimes not committed by the accused.
The saying goes that “probabilities are counter-intuitive”. A simple problem might appear to have a straightforward answer at first glance, but only do we later learn, through careful explanation, that our intuition has been subverted. Indeed, human intuition is rarely to be trusted when it comes to making probabilistic judgments, especially under time pressure. Even those trained in statistics, like most readers of Significance, can fall prey to cognitive biases when making snap decisions.
In December 1971, two years after the successful Apollo 11 moon landing, US President Richard Nixon launched a “war on cancer” with the signing of the National Cancer Act. Since that time, billions of dollars and thousands of careers have been devoted to finding ways to prevent or cure the disease. But the fight is not yet over.
Bradley Efron (pictured) was awarded the 2018 International Prize in Statistics for the creation of the “bootstrap”, a method that “transformed science’s ability to use and understand data and helped usher in the era of data analysis through computing”. But what is the bootstrap? James J. Cochran explains.
Harmful alcohol consumption is known to cause several diseases and many deaths. A study published in the Lancet this August has presented the latest figures, pertaining to the year 2016.1 The authors estimate that alcohol answers for approximately 7% of all male and 2% of all female deaths, as well as 6% of male and 2% of female disability-adjusted life year losses. Moreover, respective estimates were presented for all countries over several years. But these estimates seem to be too high.
