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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."

Solar-powered drip irrigation enhances food security in the Sudano–Sahel documents a field research project which found that: "solar-powered drip irrigation significantly augments both household income and nutritional intake, particularly during the dry season, and is cost effective compared to alternative technologies" Over the decades, irrigation has been shown to greatly increase agricultural productivity. Drip irrigation is spreading rapidly in Africa, with significant benefits. "Drip irrigation delivers water (and fertilizer) directly to the roots of plants, thereby improving soil moisture conditions; in some studies, this has resulted in yield gains of up to 100%, water savings of up to 40–80%, and associated fertilizer, pesticide, and labor savings over conventional irrigation systems" The solar-powered systems, however, look to offer the potential for even better results. From the study on impacts of PVDI systems it was reported: "The women’s agricultural group members utilizing the PVDI systems became strong net producers in vegetables with extra income earned from sales, significantly increasing their purchases of staples, pulses, and protein during the dry season, and oil during the rainy season. Finally, survey respondents were asked how frequently they were unable to meet their household food needs. Based on the frequency and most recent incident, households were assigned a food insecurity score ranging from zero (no problems during the previous year) to one (perpetually unable to meet food needs). This score changed significantly for project beneficiaries, as they were 17% less likely to feel chronically food-insecure. In short, the PVDI systems had a remarkable effect on both year-round and seasonal food access."

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.