• Confidence Intervals and Hypothesis Testing
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Confidence Intervals, Hypothesis Testing, and Correlation

Confidence Intervals, Hypothesis Testing, and Correlation

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In this course, students will understand the concept of confidence intervals as a way to recognize the uncertainty inherent to sampling, providing ranges of plausible values for population parameters and avoiding misinterpretations about the probability of containing the true value.

The module also covers hypothesis testing, explaining how to weigh the Null Hypothesis ($H_0$) against the Alternative Hypothesis ($H_1$) and how to grasp decision risks, including Type I (false positive) and Type II (false negative) errors, ensuring statistical analyses are rigorous and well-grounded.

Finally, the module explores correlation between variables, teaching how to quantify the strength and direction of associations using the Pearson and Spearman coefficients, with attention to the rule that correlation does not imply causation, the use of scatter plots, and advanced techniques such as the Bootstrap. The focus is on integrating these tools to improve relationship analysis, measure significance, and quantify uncertainty, supporting informed decisions.

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