Behaviour support technology assists people in organising their daily activities and changing their behaviour. A fundamental notion underlying such supportive technology is that of compliance with behavioural norms: do people indeed perform the desired behaviour? Existing technology employs a rigid implementation of compliance: a norm is either satisfied or not. In practice however, behaviour change norms are less strict: E.g., is a new norm to do sports at least three times a week complied with if it is occasionally only done twice a week? To address this, in this paper we formally specify probabilistic norms through a variant of feature diagrams, enabling a hierarchical decomposition of the desired behaviour and its execution frequencies. Further, we define a new notion of probabilistic norm compliance using a formal hypothesis testing framework. We show that probabilistic norm compliance can be used in a real-world setting by implementing and evaluating our semantics with respect to an existing daily behaviour dataset.