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In visual discovery, hypotheses that can be tested often relate to identifying patterns, trends, or anomalies in a given data set. For instance, one might hypothesize that a certain trend exists within the data, or that a specific anomaly will occur under certain conditions. The hypothesis could also be about the correlation between different data points or variables. The exact nature of the hypothesis would depend on the specific data set and the questions being explored.
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Visual discovery is the most complicated quadrant because it consists of two categories: testing a hypothesis and mining for patterns, trends and anomalies. Berinato says: "The former is focused, whereas the latter is more flexible. The bigger and more complex the data, and the less you know going in, the more open-ended the work."
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