Proxy Means Testing (PMT) is a popular method to target the poor in developing countries. PMT usually relies on survey-based consumption data and assumes random measurement errors – an assumption that has been challenged by recent literature. Using a survey experiment conducted in Tanzania, this paper brings causal evidence on the impact of non-random errors on PMT performances. Results show that non-random errors bias the coefficients from PMT models, resulting in a 5 to 27 percent reduction in PMT predictive performances. Moreover, non-random errors induce a 10 to 34 percent increase in the incidence of targeting errors when poverty is defined in absolute terms. More reassuringly, impacts on the ranking of households are smaller and essentially non-significant. Taken together, these results indicate that PMT performances are quite vulnerable to non-random errors when the objective is to target absolutely poor households, but remain largely unaffected when the objective is to target a fixed share of the population.