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174 Bright future of applied statisticsDuring my sabbatical we developed preliminary solutions that improvedprecision and aided in the removal of systematic biases from microarray data(Irizarry, 2003). I was aware that hundreds, if not thousands, of other scientistswere facing the same problematic data and were searching for solutions. ThereforeI was also thinking hard about ways in which I could share whatever solutionsI developed with others. During this time I received an email from RobertGentleman asking if I was interested in joining a new software project for thedelivery of statistical methods for genomics data. This new collaboration eventuallybecame the Bioconductor project (http://www.bioconductor.org),which to this day continues to grow its user and developer base (Gentlemanet al., 2004). Bioconductor was the perfect vehicle for having the impact thatmy department had encouraged me to seek. With Ben Bolstad and otherswe wrote an R package that has been downloaded tens of thousands of times(Gautier et al., 2004). Without the availability of software, the statisticalmethod would not have received nearly as much attention. This lesson servedme well throughout my career, as developing software packages has greatlyhelped disseminate my statistical ideas. The fact that my department andschool rewarded software publications provided important support.The impact statisticians have had in genomics is just one example ofour field’s accomplishments in the 21st century. In academia, the numberof statisticians becoming leaders in fields such as environmental sciences,human genetics, genomics, and social sciences continues to grow. Outsideof academia, sabermetrics has become a standard approach in severalsports (not just baseball) and inspired the Hollywood movie MoneyBall. A PhD statistician led the team that won the Netflix million dollarprize (http://www.netflixprize.com/). Nate Silver (http://mashable.com/2012/11/07/nate-silver-wins/) proved the pundits wrong by onceagain using statistical models to predict election results almost perfectly.R has become a widely used programming language. It is no surprise thatstatistics majors at Harvard have more than quadrupled since 2000 (http://nesterko.com/visuals/statconcpred2012-with-dm/] and that statisticsMOOCs are among the most popular (http://edudemic.com/2012/12/the-11-most-popular-open-online-courses/).The unprecedented advance in digital technology during the second halfof the 20th century has produced a measurement revolution that is transformingscience. Scientific fields that have traditionally relied upon simpledata analysis techniques have been turned on their heads by these technologies.Furthermore, advances such as these have brought about a shift fromhypothesis-driven to discovery-driven research. However, interpreting informationextracted from these massive and complex datasets requires sophisticatedstatistical skills as one can easily be fooled by patterns that arise bychance. This has greatly elevated the importance of our discipline in biomedicalresearch.