  Introduced in 1962, the Langlie procedure is one of the most popular
approaches to sensitivity testing. It aims to estimate an unknown sensitivity
distribution based on the outcomes of binary trials. Officially recognized by
the U.S. Department of Defense, the procedure is widely used both in civil and
military industry. It first provides an experimental design for how the binary
trials should be conducted, and then estimates the sensitivity distribution via
maximum likelihood under a simple parametric model like logistic or probit
regression. Despite its popularity and longevity, little is known about the
statistical properties of the Langlie procedure, but it is well-established
that the sequence of inputs tend to narrow in on the median of the sensitivity
distribution. For this reason, the U.S. Department of Defense's protocol
dictates that the procedure is only appropriate for estimating the median of
the distribution, and no other quantiles. This begs the question of whether the
parametric model assumption can be disposed of altogether, potentially making
the Langlie procedure entirely nonparametric, much like the Robbins-Monro
procedure. In this paper we answer this question in the negative by proving
that when the Langlie procedure is employed, the sequence of inputs converges
with probability zero.
