At the other end of the spectrum, critics condemn the lack of theory, i.e., humanistic values, such as deconstructionism, relativism, and poststructuralism, in the usage of DH tools, like maps and topic models (Drucker Hall Liu). They argue that methodological debates are more easily resolved than theoretical ones and that practitioners require guidance based on positivist rather than theoretical ideas (Scheinfeldt Raab).

Critics at one end of the opinion spectrum disavow theory (Anderson). The question of how to incorporate theory into research has been a recurring narrative in the Digital Humanities (DH). Our presented approach helps the Digital Humanities bridge the gap between the qualitative and quantitative camp, advance understanding of structures resulting from human activity, and increase scientific credibility. To falsify or to support a theory, it has to be tested in different settings. Given our specific setting, including choice of data and operationalisation of variables, we do not find enough evidence to claim that opinion leaders contributed via personal visits and letters to the adoption of Protestantism. We illustrate our approach with the example of the European Reformation, where we test a theory on the role of opinion leaders for the adoption of Protestantism with a logistic regression model. We address this problem by using theory to generate hypotheses for statistical models. Instead, we should reduce the number of hypothesis tests to only test meaningful ones. This practice is prone to mistaking random patterns for real ones.

The Digital Humanities face the problem of multiple hypothesis testing: Evermore hypotheses are tested until a desired pattern has been found.

Testing for assumptions data dredging