In an instrumental variable model, the score statistic can be bounded for any alternative in parts of the parameter space. These regions involve a constraint on the first-stage regression coefficients and the reduced-form covariance matrix. Consequently, the Lagrange Multiplier test can have power close to size, despite being efficient under standard asymptotics. This information loss limits the power of conditional tests which use only the Anderson-Rubin and the score statistic. The conditional quasi-likelihood ratio test also suffers severe losses because it can be bounded for any alternative. A necessary condition for drastic power loss to occur is that the Hermitian of the reduced-form covariance matrix has eigenvalues of opposite signs. These cases are denoted impossibility designs (ID). We show this happens in practice, by applying our theory to the problem of inference on the intertemporal elasticity of substitution (IES). Of eleven countries studied by Yogo (2004} and Andrews (2016), nine are consistent with ID at the 95\% level.