Navigating Analytical Methodologies in Data Analysis

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The chapter delves into the considerations data analysts must weigh when selecting analytical methodologies for new business questions, including factors like assumptions, complexity, certainty levels, and margin of error. It discusses making decisions based on statistical significance and communicating results effectively, emphasizing the importance of understanding stakeholders' knowledge levels. The conversation also explores the complexities of method selection, the need for humility, and strategies to navigate conversations with stakeholders to uncover their true questions.

From running a controlled experiment to running a linear regression. From eyeballing a line chart to calculating the correlation of first differences. From performing a cluster analysis because that’s what the business partner asked for to gently probing for details on the underlying business question before agreeing to an approach. There are countless analytical methodologies available to the analyst, but which one is best for any given situation? Simon Jackson from Hypergrowth Data joined Moe, Julie, and Tim on the latest episode to try to get some clarity on the topic. We haven’t figured out which methodology to use to analyze whether we succeeded, so you’ll just have to listen and judge for yourself. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.