Why Most Published Research Findings Are False A Research Paper by John P.A. Ioannidis journals.plos.org There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. researchsciencetruth
Feature parity An Article martinfowler.com Whilst Feature Parity often sounds like a reasonable proposition, we have learnt the hard way that people greatly underestimate the effort required, and thus misjudge the choice between this and the other alternatives. For example even just defining the 'as is' scope can be a huge effort, especially for legacy systems that have become core to the business. Most legacy systems have 'bloated' over time, with many features unused by users (50% according to a 2014 Standish Group report) as new features have been added without the old ones being removed. Workarounds for past bugs and limitations have become 'must have' requirements for current business processes, with the way users work defined as much by the limitations of legacy as anything else. Rebuilding these features is not only waste it also represents a missed opportunity to build what is actually needed today. These systems were often defined 10 or 20 years ago within the constraints of previous generations of technology, it very rarely makes sense to replicate them 'as is'. softwarefeaturesrepair