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n part one of this two-part series, I described the need for empiricism in root cause analysis (RCA). Now, I’ll explain how to achieve empiricism when performing a RCA by combining the scientific method and graphical explorations of data.

The statistician John Tukey believed data should be viewed graphically and came up with ideas as a basis for further testing. He called this exploratory data analysis (EDA) in contrast with confirmatory data analysis (CDA), where the objective is to evaluate a hypothesis^.1 The scientific method can be supported by the use of Tukey’s EDA to generate data that can be empirically investigated. Tukey’s EDA explores data graphically to gain new insights.

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