  Large-scale randomised experiments have become a standard tool for developing
products and improving user experience. To reduce losses from shipping harmful
changes experimental results are, in practice, often checked repeatedly, which
leads to inflated false alarm rates. To alleviate this problem, one can use
sequential testing techniques as they control false discovery rates despite
repeated checks. While multiple sequential testing methods exist in the
literature, they either restrict the number of interim checks the experimenter
can perform or have tuning parameters that require calibration. In this paper,
we propose a novel sequential testing method that does not limit the number of
interim checks and at the same time does not have any tuning parameters. The
proposed method is new and does not stem from existing experiment monitoring
procedures. It controls false discovery rates by ``inverting'' a bound on the
threshold crossing probability derived from a classical maximal inequality. We
demonstrate both in simulations and using real-world data that the proposed
method outperforms current state-of-the-art sequential tests for continuous
test monitoring. In addition, we illustrate the method's effectiveness with a
real-world application on a major online fashion platform.
