Declining public trust in official statistics and indicators is frequently highlighted as a key obstacle to reasoned debate on policy options and governance choices. The potentially harmful impacts of Big Data and an alleged “post-truth” era have further accentuated such concerns. To remain trusted and credible, statistical institutions must safeguard their authority as sources of independent and scientifically sound indicators, while at the same time being innovative, to ensure the relevance of the indicators. However, this article argues that, in addition to this trust-building work, embracing mistrust and distrust is essential if indicators are to be relevant and influential. By unpacking the notion of trust, the article illustrates ways in which mistrust and distrust can serve as resources rather than mere threats to the credibility and authority of official statistics. For further empirical work, a conceptual framework consisting of three dimensions of trust and a distinction between mistrust and distrust is proposed and illustrated with concrete examples from indicator work. The conclusions suggest ways for statistical institutions to adjust their strategies so as to maintain trust via a more nuanced understanding of the multiple dimensions of trust, mistrust and distrust.
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