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This is the largest nationally representative study to date of the toll taken by sepsis and pneumonia, two conditions often caused by deadly microbes, including the antibiotic-resistant bacteria MRSA. Such infections can lead to longer hospital stays, serious complications and even death. "In many cases, these conditions could have been avoided with better infection control in hospitals," said Ramanan Laxminarayan, Ph.D., principal investigator for Extending the Cure, a project examining antibiotic resistance based at the Washington, D.C. think-tank Resources for the Future. "Infections that are acquired during the course of a hospital stay cost the United States a staggering amount in terms of lives lost and health care costs," he said. "Hospitals and other health care providers must act now to protect patients from this growing menace." Laxminarayan and his colleagues analyzed 69 million discharge records from hospitals in 40 states and identified two conditions caused by health care-associated infections: sepsis, a potentially lethal systemic response to infection and pneumonia, an infection of the lungs and respiratory tract. The researchers looked at infections that developed after hospitalization. They zeroed in on infections that are often preventable, like a serious bloodstream infection that occurs because of a lapse in sterile technique during surgery, and discovered that the cost of such infections can be quite high: For example, people who developed sepsis after surgery stayed in the hospital 11 days longer and the infections cost an extra $33,000 to treat per person. Even worse, the team found that nearly 20 percent of people who developed sepsis after surgery died as a result of the infection. "That's the tragedy of such cases," said Anup Malani, a study co-author, investigator at Extending the Cure, and professor at the University of Chicago. "In some cases, relatively healthy people check into the hospital for routine surgery. They develop sepsis because of a lapse in infection control—and they can die." The team also looked at pneumonia, an infection that can set in if a disease-causing microbe gets into the lungs—in some cases when a dirty ventilator tube is used. They found that people who developed pneumonia after surgery, which is also thought to be preventable, stayed in the hospital an extra 14 days. Such cases cost an extra $46,000 per person to treat. In 11 percent of the cases, the patient died as a result of the pneumonia infection.

King's comments weren't recorded, but a staffer for Media Matters, who heard the comments, provided TPMmuckraker with an account. The staffer, who requested anonymity because she's not a communications specialist, said that King, an extreme right-winger with a reputation for eyebrow-raising rhetoric, appeared as a surprise guest speaker on an immigration panel at the conservative conference. During his closing remarks, King veered into a complaint about high taxes, and said he could "empathize" with the man who flew a plane into an IRS building last week. During the question and answer session, the Media Matters staffer asked King to clarify his comment, reminding him of his sworn duty to protect the American people from all sworn enemies, foreign and domestic. In response, said the staffer, King gave a long and convoluted answer about having been personally audited by the IRS, and ended by saying he intended to hold a fundraiser to help people "implode" their local IRS office.

In his new book, "The Double Helix and the Law of Evidence" (Harvard University Press), Kaye focuses on the intersection of science and law, and emphasizes that DNA evidence is merely information. "There's a popular perception that with DNA, you get results," Kaye said. "You're either guilty or innocent, and the DNA speaks the truth. That goes too far. DNA is a tool. Perhaps in many cases it's open and shut, in other cases it's not. There's ambiguity."

One of the book's key themes is that using science in court is hard to do right. "It requires lawyers and judges to understand a lot about the science," Kaye noted. "They don't have to be scientists or technicians, but they do have to know enough to understand what's going on and whether the statements that experts are making are well-founded. The lawyers need to be able to translate that information into a form that a judge or a jury can understand." Kaye also believes that lawyers need to better understand statistics and probability, an area that has traditionally been neglected in law school curricula. His book attempts to close this gap in understanding with several sections on genetic science and probability. The book also contends that scientists, too, have contributed to the false sense of certainty, when they are so often led by either side of one particular case to take an extreme position. Scientists need to approach their role as experts less as partisans and more as defenders of truth. Aiming to be a definitive history of the use of DNA evidence, "The Double Helix and the Law of Evidence" chronicles precedent-setting criminal trials, battles among factions of the scientific community and a multitude of issues with the use of probability and statistics related to DNA. From the Simpson trial to the search for the last Russian Tsar, Kaye tells the story of how DNA science has impacted society. He delves into the history of the application of DNA science and probability within the legal system and depicts its advances and setbacks.

Statistical validation of results, as Shaffer described it, simply involves testing the null hypothesis: that the pattern you detect in your data occurs at random. If you can reject the null hypothesis—and science and medicine have settled on rejecting it when there's only a five percent or less chance that it occurred at random—then you accept that your actual finding is significant. The problem now is that we're rapidly expanding our ability to do tests. Various speakers pointed to data sources as diverse as gene expression chips and the Sloan Digital Sky Survey, which provide tens of thousands of individual data points to analyze. At the same time, the growth of computing power has meant that we can ask many questions of these large data sets at once, and each one of these tests increases the prospects than an error will occur in a study; as Shaffer put it, "every decision increases your error prospects." She pointed out that dividing data into subgroups, which can often identify susceptible subpopulations, is also a decision, and increases the chances of a spurious error. Smaller populations are also more prone to random associations. In the end, Young noted, by the time you reach 61 tests, there's a 95 percent chance that you'll get a significant result at random. And, let's face it—researchers want to see a significant result, so there's a strong, unintentional bias towards trying different tests until something pops out. Young went on to describe a study, published in JAMA, that was a multiple testing train wreck: exposures to 275 chemicals were considered, 32 health outcomes were tracked, and 10 demographic variables were used as controls. That was about 8,800 different tests, and as many as 9 million ways of looking at the data once the demographics were considered.