We present cautionary examples of what can go wrong when assumptions behind
statistical procedures are insufficiently examined, even when the analysis is
performed by highly reputed and otherwise careful practitioners. Our examples
come from a series of recent papers by Christakis and Fowler that claim to have
demonstrated the existence of transmission via social networks of various
personal characteristics, including obesity, smoking cessation, happiness, and
loneliness. Those papers also assert that such influence extends to three
degrees of separation in social networks.
We prove that in every bipartite Cayley graph of every non-amenable group,
there is a perfect matching that is obtained as a factor of independent uniform
random variables. We also discuss expansion properties of factors and improve
the Hoffman spectral bound on independence number of finite graphs.
Given a homogeneous Poisson process on R^d with intensity L, we prove that it
is possible to partition the points into two sets, as a deterministic function
of the process, and in an isometry-equivariant way, so that each set of points
forms a homogeneous Poisson process, with any given pair of intensities summing
to L. In particular, this answers a question of Ball, who proved that in d=1,
the Poisson points may be similarly partitioned (via a translation-equivariant
function) so that one set forms a Poisson process of lower intensity, and asked
whether the same was possible for all d.