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Understanding Sample Sizes: Let Them Do the Heavy Lifting
Data Science   Latest   Machine Learning

Understanding Sample Sizes: Let Them Do the Heavy Lifting

Last Updated on January 25, 2024 by Editorial Team

Author(s): Deepak Chopra | Talking Data Science

Originally published on Towards AI.

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This article is a continuation of the series on ‘Measuring Marketing Effectiveness’, in case you missed the previous parts, here is a quick recap:

· Part 1: Themes: Marketing incrementality, Experimental design, Test/Control, AB testing — Best-in-class methodologies.

· Part 2: Themes: Hypothesis (Null and Alternate) formulation.

· Part 3: Themes: Zoomed in on “Statistical Significance”.

· Part 4: Themes: Understanding Statistical Power, Type-1 and 2 errors.

In the realm of research and data analysis, the concept of ‘sample size’ plays a pivotal role. It’s the backbone that supports the validity and reliability of any study or experiment. But what exactly is a sample size? Why is it so crucial? And how can we determine the appropriate sample size for a study?

Whether you’re a seasoned data scientist, a budding researcher, or a curious reader, understanding the nuances of sample size is essential. It’s the bridge that connects the realm of theoretical assumptions with the reality of empirical data. It’s the tool that helps us make sense of the world in a quantifiable and measurable way.

In this article, we will delve into the world of sample sizes. We’ll explore its importance, the factors that influence it, and the impact it has on the outcomes of… Read the full blog for free on Medium.

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