Performance testing is essential to ensure that software systems sustain realistic workloads and meet expected service levels under load. While prior research has extensively investigated performance testing methodologies, workload modeling, and tool support, there is limited consolidated knowledge on which recurring bad practices may silently compromise the reliability of performance evaluations. In this paper, we present the first catalog of bad practices in performance test code, derived through a gray literature review of practitioner sources. Building on this catalog, we introduce PTLinter, an automated tool that detects instances of these bad practices in Apache JMeter test artifacts. We apply PTLinter to the E2EGit dataset, analyzing 244 open-source test artifacts to investigate the prevalence, evolution, and lifespan of performance test bad practices, as well as the reasons behind their introduction, removal, and worsening over time. The study identified 10 bad practices and found they are widespread, often introduced near major releases during periods of high developer workload.