  A key feature of a sequential study is that the actual sample size is a
random variable that typically depends on the outcomes collected. While
hypothesis testing theory for sequential designs is well established, parameter
and precision estimation is less well understood. Even though earlier work has
established a number of ad hoc estimators to overcome alleged bias in the
ordinary sample average, recent work has shown the sample average to be
consistent. Building upon these results, by providing a rate of convergence for
the total variation distance, it is established that the asympotic distribution
of the sample average is normal, in almost all cases, except in a very specific
one where the stopping rule is deterministic and the true population mean
coincides with the cut-off between stopping and continuing. For this
pathological case, the Kolmogorov distance with the normal is found to equal
0.125. While noticeable in the asymptotic distribution, simulations show that
there fortunately are no consequences for the coverage of normally-based
confidence intervals.
