Abstract
In this perspective, we argue that single-design, single-analysis studies leave too much uncertainty unresolved to provide sufficiently informative answers to research questions. Such single-shot studies remain common across many disciplines, yet they reflect numerous explicit and implicit design and analytical decisions. Yet without systematically examining the consequences of these choices, researchers cannot determine the extent to which empirical findings depend on them. We contend that uncertainties arising from variability in research design and analysis are an unavoidable feature of empirical inquiry and a major source of the persistent replication problems, conflicting and seemingly irreconcilable findings, and limited cumulative knowledge observed across many fields. To address this challenge, we present a unifying framework for existing methodologies that allow researchers to systematically explore, reduce, and integrate these often-overlooked uncertainties underlying empirical claims. We argue that many of the necessary methods already exist across disciplines, but their broader impact has been limited by a lack of a shared conceptual framework and interdisciplinary integration. By balancing hypothesis testing with exploration and theory building, and by systematically incorporating Space Analysis tools into research practice, scientific fields can move beyond isolated single-shot studies toward systematic research programmes capable of producing more reliable, robust, and cumulative scientific knowledge.