Exploratory data analysis

Exploratory data analysis helps you understand your data before any formal hypothesis testing. Start by examining basic distributions to see how values are spread and to identify outliers, which could signal errors or interesting edge cases. Simple visualizations like histograms and box plots often reveal user behavior more clearly than complex models applied to poorly understood data.

Segmenting data uncovers patterns that averages hide. Different user groups can behave in opposite ways: new users may struggle with features that experienced users enjoy, and usage can vary between mobile and desktop or across geographies. Breaking data into meaningful segments often explains trends that aggregate analysis misses.

Time-based exploration is also crucial. Metrics plotted over different time scales can reveal trends hidden by daily fluctuations, while seasonal patterns, day-of-week, and hour-of-day effects show when users actually engage. Understanding these rhythms is important before labeling features as successful or unsuccessful.