In the concluding segment of our two-part series on “Inference for Quantitative Variables”, this episode dives into the finer details of the normal distribution. Building on our exploration of sample means, we shift our attention to differences in means and correlations. The discussion methodically unpacks the calculation of standard error when comparing two means, highlighting the shift from normal to t-distribution with illustrative examples.

Listeners will deepen their understanding of how to estimate population standard deviations from sample data, and the slight adjustments in formulas adapted for various statistical tests. This episode not only solidifies the concepts previously discussed but also demonstrates their practical application through engaging examples, both hypothetical and real.

Additionally, we delve into hypothesis testing and confidence intervals, illustrating how these statistical tools are used to interpret research data. By the end of this segment, you will be better equipped to determine degrees of freedom, calculate t-values, and understand p-values in hypothesis testing, enhancing your ability to analyze and reason statistically. Tune in for an enlightening finale that promises to strengthen your grasp of statistical inference in quantitative research.

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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