Parametric vs. Non-Parametric Tests: A Comprehensive Guide for Data Scientists

Learn to understand the difference and use them properly

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Data science is a rapidly growing field that relies heavily on statistical techniques to analyze and make sense of complex datasets. A crucial part of this process is hypothesis testing, which helps to determine if the results obtained from a sample can be generalized to the population.

In this blog post, we will explore the differences between parametric and non-parametric tests, provide examples to better understand their use cases, and summarize the key takeaways.

Table of Contents

1. Understanding Hypothesis Testing
2. Parametric Tests: Definition and Examples
3. Non-Parametric Tests: Definition and Examples
4. Choosing Between Parametric and Non-Parametric Tests
5. Key Takeaways

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1. Understanding Hypothesis Testing

Hypothesis testing is a statistical method used to determine the likelihood that a given…