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"How old you are matters, but beyond that it's your interpretation that has far-reaching implications for the process of aging," said Markus H. Schafer, a doctoral student in sociology and gerontology who led the study. "So, if you feel old beyond your own chronological years you are probably going to experience a lot of the downsides that we associate with aging. "But if you are older and maintain a sense of being younger, then that gives you an edge in maintaining a lot of the abilities you prize." Schafer and co-author Tetyana P. Shippee, a Purdue graduate who is a research associate at Purdue's Center on Aging and the Life Course, compared people's chronological age and their subjective age to determine which one has a greater influence on cognitive abilities during older adulthood. Nearly 500 people ages 55-74 were surveyed about aging in 1995 and 2005 as part of the National Survey of Midlife Development in the United States. In 1995, when people were asked what age do you feel most of the time, the majority identified with being 12 years younger than they actually were. "We found that these people who felt young for their age were more likely to have greater confidence about their cognitive abilities a decade later," Schafer said. "Yes, chronological age was important, but the subjective age had a stronger effect. "What we are not sure about is what comes first. Does a person's wellness and happiness affect their cognitive abilities or does a person's cognitive ability contribute to their sense of wellness. We are planning to address this in a future study."

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."

By reworking a theory first proposed by physicists in the 1920s, the researchers discovered a new way to predict important characteristics of a new material before it's been created. The new formula allows computers to model the properties of a material up to 100,000 times faster than previously possible and vastly expands the range of properties scientists can study. "The equation scientists were using before was inefficient and consumed huge amounts of computing power, so we were limited to modeling only a few hundred atoms of a perfect material," said Emily Carter, the engineering professor who led the project. "But most materials aren't perfect," said Carter, the Arthur W. Marks '19 Professor of Mechanical and Aerospace Engineering and Applied and Computational Mathematics. "Important properties are actually determined by the flaws, but to understand those you need to look at thousands or tens of thousands of atoms so the defects are included. Using this new equation, we've been able to model up to a million atoms, so we get closer to the real properties of a substance." By offering a panoramic view of how substances behave in the real world, the theory gives scientists a tool for developing materials that can be used for designing new technologies. Car frames made from lighter, strong metal alloys, for instance, might make vehicles more energy efficient, and smaller, faster electronic devices might be produced using nanowires with diameters tens of thousands of times smaller than that of a human hair.