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Data Science: Mindset, Methodologies, and Misconceptions by Zacharias Voulgaris PhD

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Chapter 7 Sensitivity Analysis of Experiment Conclusions

Experiment conclusions may be convincing, but this does not make them immune to instability, especially if they stem from a small or a somewhat unrepresentative dataset. To tackle this fairly serious matter, we employ a particular method called sensitivity analysis, a practice common to all scientific experiments, but not particularly common to data science. In this chapter, we will look at why it is very important in our field, how it is related to the well-known butterfly effect, the most common ways of performing sensitivity analysis using resampling, and how “what if” questions fit into all of this.

The Importance of Sensitivity Analysis

No matter how well we design and conduct our ...

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