 The most intuitive case of a testing intervention might be the following one: \(X=x_1\) describes an actual state of affairs while \(X=x_2\) describes a possible counterfactual state of affairs. For instance, suppose that Boyle’s gas law —\(p \cdot V = N \cdot k cdot T\)—is true for the actual temperature of a gas \(g\) of 30°C. According to the testing intervention condition, Boyle’s gas law is stable if it also holds for (counterfactual) temperature of, say, 40°C. One might call this kind of invariance “minimal” invariance. Contrary to Lange’s theory, minimal invariance is necessary and sufficient for the statement to count as invariant. Notice, however, that invariance under variations of background variables is neither necessary nor sufficient for being minimally invariant (cf. Woodward 2003, 248; EG1, 7f.).