Exploratory data analysis (EDA) is a strategy of data analysis that emphasizes maintaining an open mind to alternative possibilities. EDA is a philosophy or an attitude about how data analysis should be carried out, rather than being a fixed set of techniques. It is difficult to obtain a clear-cut answer from “messy” human phenomena, and thus the exploratory character of EDA is very suitable to psychological research. This research tradition was founded by John Tukey, who often relates EDA to detective work. In EDA, the role of the researcher is to explore the data in as many ways as possible until a plausible “story” emerges. A detective does not collect just any information. Instead, he or she collects clues related to the central question of the case. By the same token, EDA is not “fishing” or “torturing” the data set until it confesses. Rather, it is a systematic way to investigate relevant information from multiple perspectives. Tukey emphasizes the role of data analysis in research, rather than mathematics, statistics, and probability. Mathematics is secondary in the sense that it is a tool for understanding the data. Classical statistics aims to infer from the sample to the population based on the probability as the relative frequency in the long run. However, in many stages of inquiry, the working questions are non-probabilistic and the focal point should be the data at hand rather than the probabilistic inference in the long run. Hence, prematurely adopting a specific statistical model would hinder the researchers from considering different possible solutions. Because EDA endorses open-mindedness and triangulation, it is not a standalone approach. Rather, it complements traditional confirmatory data analysis (CDA) by generating a working hypothesis, as well as spotting outliers and assumption violations that might invalidate CDA. Additionally, it can also be operated with Bayesian statistics and resampling side by side. With the advent of high-power computers and voluminous data, many exploratory techniques have been developed in data science. These methods are known as data mining. Because it is tedious or even impossible to detect the data patterns when the sample size is extremely large or there are too many variables (this problem is called the “curse of dimensionality”), some data miners use machine learning to explore alternate routes for understanding the data. There are different taxonomies of EDA. Traditionally, EDA comprises residual analysis, data re-expression, resistant procedures, and data visualization. With the advance of high-power computing and big data analytics, the alternate taxonomy is goal oriented, namely, clustering, variable screening, and pattern recognition.