  Experimental testing is vital in the optimization of web applications, and as
such A/B testing has been widely adopted as a methodology for determining
optimal content for many web applications. While some testing platforms provide
sequentially valid inferences, a large proportion of online tests still utilize
traditional statistical tests that do not allow for interim "peeking" at the
data or extending the test past its proposed sample size.
  In this paper we develop results useful for the sequential analysis of large
scale experiments. In particular, the properties of sequences of maximum
likelihood and generalized method of moments estimators are examined. This
leads to new tests of odds ratios and relative risks for binary outcomes. For
continuous and ordinal outcome we develop a test of mean difference and a
non-parametric test of Area Under the Curve (AUC). Additionally, multivariate
versions of these tests are proposed.
