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This lecture covers the concept of break points in statistics, where a small fraction of contamination can completely destabilize a statistic, and the importance of resistant statistics that do not change much with disrupted data. It also delves into the correlation analysis, emphasizing that correlation does not imply causality and can be misleading. Practical examples and visual representations are used to illustrate these concepts.