In a sequential trial, the data collector is allowed to take intermediate looks at the data. After each intermediate look, he can decide, based on the data observed so far and a prescribed stopping rule, if the trial is stopped or continued. Stopping a trial early is potentially beneficial for economic and ethical reasons in e.g. a clinical study. The existing literature on sequential trials has reported that simple conventional estimators, such as the sample mean, become biased in the presence of observation based stopping rules. However, very recently, Molenberghs and colleagues have taken away some of this concern by showing that in many cases the bias, caused by stopping rules, vanishes quickly as the number of collected data increases. Building upon the insights obtained by Molenberghs and colleagues, we will provide a theoretical underpinning of the fact that conventional estimation remains in many important cases legitimate in a sequential trial. More precisely, we seek to establish Berry-Esseen type inequalities in sequential analysis that justify the use of confidence intervals based on conventional estimators. Also, the implications for hypothesis testing will be investigated. To this end, we will use mathematical techniques from probability, e.g. Stein's method, and approach theory, a topological theory pioneered by R. Lowen, which has already been useful in the theory of probability metrics, central limit theory, and estimation with contaminated data.