Byte addressable, Non-Volatile Memory (NVM) is emerging as a revolutionary technology that provides near-DRAM performance and scalable memory capacity. To facilitate the usability of NVM, new programming frameworks have been proposed to automatically or semi-automatically maintain crash-consistent data structures, relieving much of the burden of developing persistent applications from programmers. While these new frameworks greatly improve programmer productivity, they also require many runtime checks for correct execution on persistent objects, which significantly affect the application performance. With a characterization study of various workloads, we find that the overhead of these persistence checks in these programmer-friendly NVM frameworks can be substantial and reach up to 214%. Furthermore, we find that programs nearly always access exclusively either a persistent or a non-persistent object at a given site, making the behavior of these checks highly predictable. In this paper, we propose QuickCheck, a technique that biases persistence checks based on their expected behavior, and exploits speculative optimizations to further reduce the overheads of these persistence checks. We evaluate QuickCheck with a variety of data intensive applications such as a key-value store. Our experiments show that QuickCheck improves the performance of a persistent Java framework on average by 48.2% for applications that do not require data persistence, and by 8.0% for a persistent memcached implementation running YCSB.