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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.

The team observed viruses as they evolved over hundreds of generations to infect bacteria. They found that when the bacteria could evolve defences, the viruses evolved at a quicker rate and generated greater diversity, compared to situations where the bacteria were unable to adapt to the viral infection. The study shows, for the first time, that the American evolutionary biologist Leigh Van Valen was correct in his 'Red Queen Hypothesis'. The theory, first put forward in the 1970s, was named after a passage in Lewis Carroll's Through the Looking Glass in which the Red Queen tells Alice, 'It takes all the running you can do to keep in the same place'. This suggested that species were in a constant race for survival and have to continue to evolve new ways of defending themselves throughout time. Dr Steve Paterson, from the University's School of Biosciences, explains: "Historically, it was assumed that most evolution was driven by a need to adapt to the environment or habitat. The Red Queen Hypothesis challenged this by pointing out that actually most natural selection will arise from co-evolutionary interactions with other species, not from interactions with the environment. "This suggested that evolutionary change was created by 'tit-for-tat' adaptations by species in constant combat. This theory is widely accepted in the science community, but this is the first time we have been able to show evidence of it in an experiment with living things." Dr Michael Brockhurst said: "We used fast-evolving viruses so that we could observe hundreds of generations of evolution. We found that for every viral strategy of attack, the bacteria would adapt to defend itself, which triggered an endless cycle of co-evolutionary change. We compared this with evolution against a fixed target, by disabling the bacteria's ability to adapt to the virus. "These experiments showed us that co-evolutionary interactions between species result in more genetically diverse populations, compared to instances where the host was not able to adapt to the parasite. The virus was also able to evolve twice as quickly when the bacteria were allowed to evolve alongside it."

Scanning electron micrograph of the nanofossil Chiasmolithus from about 60 million years ago. This genus arose after the Cretacious Paleogene boundary mass extinction. The size about 8 microns.

The darkness caused by the collision would impair photosynthesis and reduce nannoplankton reproduction. While full darkness did not occur, the effects in the north would have lasted for up to six months. However, with ample sunlight and large amounts of nutrients in the oceans, the populations should have bounced back, even in the North, but they did not. The researchers suggest that toxic metals that where part of the asteroid, heavily contaminated the Northern oceans and were the major factor inhibiting recovery. "Metal loading is a great potential mechanism to delay recovery," said Bralower. "Toxic levels in the parts per billions of copper, nickel, cadmium and iron could have inhibited recovery." On the one hand, the researchers considered an impact scenario causing perpetual winter and ocean acidification to explain the slow recovery, but neither explains the lag between Southern and Northern Hemispheres. Trace metal poisoning, on the other hand, would have been severe near the impact in the Northern Hemisphere. When the high temperature debris from the impact hit the water, copper, chromium, aluminum, mercury and lead would have dissolved into the seawater at likely lethal levels for plankton. Iron, zinc and manganese -- normally micronutrients -- would reach harmful levels shortly after the impact. Other metal sources might be acid-rain leached soils or the effects of wildfires. Metals like these can inhibit reproduction or shell formation. The toxic metals probably exceeded the ability of organic compounds to bind them and remove them from the system. Because nannoplankton are the base of the food chain, larger organisms concentrate any metals found in nannoplankton making the metal poisoning more effective. With the toxic metals remaining in the oceans and the lack of sunlight, the length of time for recovery might increase.