In psychological research, it is not an exceptional practice to look at the data during the course of a study. If the observed effects are greater than expected, this may lead the investigator to stop the study early. Or conversely, if the effects are smaller than expected, the researcher can make adjustments to the study, such as increasing the planned sample size, in order to still obtain the desired results. When done without planning at the start of the study and without the necessary statistical corrections, these practices can lead to an inflation of false positive research results. Such questionable research practices are therefore a driving mechanism for the replication crisis in psychology. Although designs and statistical techniques to perform such interim analyses in a scientifically correct manner have been developed for clinical studies within the medical world for about two decades, their use in psychological research remains minimal. In this research project, we propose important changes that should make such interim analyses more attractive. By making optimal use of all available information, we propose more efficient interim analyses. Moreover, we allow that the effects of interventions can differ in subpopulations.