With ever-increasing execution scale of parallel scientific simulations, potential unnoticed corruptions to scientific data during simulation make users more suspicious about the correctness of floating-point calculations than ever before. In this paper, we analyze the issue of the trust in results of numerical simulations and scientific data analytics. We first classify the corruptions into two categories, nonsystematic corruption and systematic corruption, and also discuss their origins. Then, we provide a formal definition of the trust in simulation and analytical results across multiple areas. We also discuss what kind of result accuracy would be expected from user’s perspective and how to build trust by existing techniques. We finally identify the current gap and discuss two potential research directions based on existing techniques. We believe that this paper will be interesting to the researchers who are working on the detection of potential unnoticed corruptions of scientific simulation and data analytics, in that not only does it provide a clear definition and classification of corruption as well as an in-depth survey on corruption sources, but we also discuss potential research directions/topics based on existing detection techniques.