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The Power of Experimentation in Data Science
This chapter explores the critical role of experimentation and trial and error in data science, emphasizing how high failure rates can lead to significant breakthroughs. It contrasts traditional planning with an experimental culture that fosters rapid iteration and innovation using small-scale testing like A/B tests. The discussion underscores the importance of utilizing historical data and smaller samples to effectively manage the risks and rewards of data-driven decision-making.