  We prove two results about randomised query complexity $\mathrm{R}(f)$.
First, we introduce a "linearised" complexity measure $\mathrm{LR}$ and show
that it satisfies an inner-optimal composition theorem: $\mathrm{R}(f\circ g)
\geq \Omega(\mathrm{R}(f) \mathrm{LR}(g))$ for all partial $f$ and $g$, and
moreover, $\mathrm{LR}$ is the largest possible measure with this property. In
particular, $\mathrm{LR}$ can be polynomially larger than previous measures
that satisfy an inner composition theorem, such as the max-conflict complexity
of Gavinsky, Lee, Santha, and Sanyal (ICALP 2019).
  Our second result addresses a question of Yao (FOCS 1977). He asked if
$\epsilon$-error expected query complexity $\bar{\mathrm{R}}_{\epsilon}(f)$
admits a distributional characterisation relative to some hard input
distribution. Vereshchagin (TCS 1998) answered this question affirmatively in
the bounded-error case. We show that an analogous theorem fails in the
small-bias case $\epsilon=1/2-o(1)$.
