By their very nature, malware samples employ a variety of techniques to conceal their malicious behavior and hide it from analysis tools. To mitigate the problem, a large number of different evasion techniques have been documented over the years, and PoC implementations have been collected in public frameworks, like the popular Al-Khaser. As malware authors tend to reuse existing approaches, it is common to observe the same evasive techniques in malware samples of different families. However, no measurement study has been conducted to date to assess the adoption and prevalence of evasion techniques. In this paper, we present a large-scale study, conducted by dynamically analyzing more than 180K Windows malware samples, on the evolution of evasive techniques over the years. To perform the experiments, we developed a custom Pin-based Evasive Program Profiler (Pepper), a tool capable of both detecting and circumventing 53 anti-dynamic-analysis techniques of different categories, ranging from anti-debug to virtual machine detection. To observe the phenomenon of evasion from different points of view, we employed four different datasets, including benign files, advanced persistent threat (APTs), malware samples collected over a period of five years, and a recent collection of different families submitted to VirusTotal over a one-month period.