In this engaging final episode of our three-part series on “Hypothesis Testing,” we delve into the intricate world of statistical significance, sample sizes, and hypothesis testing errors. Our host, alongside guest statisticians, breaks down complex concepts into understandable segments, focusing on how different sample statistics like means and proportions are used to make inferences about populations from samples.

The episode starts with a refresher on basic statistical terms and how they are applied in real-world scenarios, such as gender differences in divorce rates or the correlation between height and salary. Through interactive questions and examples, the discussion leads to an exploration of null and alternative hypotheses, including how to set up hypothesis tests and interpret p-values.

Listeners will gain insight into the nuances of type I and type II errors and how significance levels (alpha values) influence the outcomes of statistical tests. The conversation also covers practical examples, such as the effects of red wine on weight loss and how statistical significance might not always translate into practical significance.

This episode is not only a comprehensive review but also a critical examination of how statistical decisions can impact research outcomes. It’s a must-listen for anyone involved in research, providing the tools to better understand and implement hypothesis testing in their own studies.

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Textbook: ⁠⁠Statistics: Unlocking the Power of Data⁠⁠

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Lecture slides and additional course material can be obtained by emailing bradrfulton@gmail.com