Few textbooks on research methods offer more than a few words of advice on how to devise scientific hypotheses. Big data is conceived as a hypothesis-generator procedure, a disruptive analytical innovation that is reconfiguring ecological research. My theses are (a) the hypotheses that big-data can originate “stricto sensu” are empirical generalizations that do not provide ecological understanding, (b) empirical generalizations may encourage instrumentalist research, but cannot supply ecological explanation, and (c) generalizations emerging from data-driven research can serve as a problem-generating procedure if they are reflected in the context of the theoretical framework surrounding the research. Discovery (e.g., novel patterns shown by big-data analysis) and invention (e.g., hypotheses on mechanisms and processes conjectured by the human mind) are complementary tools in ecological research because they play different epistemological roles. Data-driven research provides a useful analytical tool, but it does not justify any epistemological or methodological paradigm shift.