We present LANCET, a self-correcting tool designed to measure the open-loop tail latency of µs-scale datacenter applications with high fan-in connection patterns. LANCET is self-correcting as it relies on online statistical tests to determine situations in which tail latency cannot be accurately measured from a statistical perspective. The workload configuration, the client infrastructure, or the application itself could, under circumstances, prevent accurate measurement. Because of its design, LANCET is also extremely easy to use. In fact, the user is only responsible for (i) configuring the workload parameters, i.e., the mix of requests and the size of the client connection pool, and (ii) setting the desired confidence interval for a particular tail latency percentile. All other parameters, including the length of the warmup phase, the measurement duration, and the sampling rate, are dynamically determined by the LANCET experiment coordinator. When available, LANCET leverages NIC-based hardware time-stamping to measure RPC end-to-end latency. Otherwise, it uses an asymmetric setup with a latency-agent that leverages busy-polling system calls to reduce the client bias. Our evaluation shows that LANCET automatically identifies situations in which tail latency cannot be determined and accurately reports the latency distribution of workloads with single-digit µs service time. For the workloads studied, LANCET can successfully report, with 95% confidence, the 99th percentile tail latency within an interval of ≤ 10µs. In comparison with state-of-the-art tools such as Mutilate and Treadmill, LANCET reports a latency cumulative distribution that is ∼20µs lower when the NIC time-stamping capability is available and ∼10µs lower when it is not.