Lecture

Statistical Hypothesis Testing: Inference and Interpretation

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In course

MATH-236: Probability and statistics II

Linear statistical methods, analysis of experiments, logistic regression. ...

Description

This lecture covers the fundamentals of statistical hypothesis testing, focusing on the process of making inferences about population parameters based on sample data. Topics include constructing confidence intervals, hypothesis testing procedures, interpreting p-values, and understanding the significance of test results. Through examples involving radon levels and coin toss experiments, the instructor illustrates how to formulate null and alternative hypotheses, compute test statistics, and make decisions based on significance levels. The lecture also addresses common pitfalls in hypothesis testing, such as interpreting results from non-random samples and distinguishing between statistical and practical significance.

Instructor

Darlene Goldstein

Official source

Ontological neighbourhood

Statistics

Statistical inference: Statistical hypothesis testing, Mathematical statistics

Related concepts (32)

Statistical hypothesis testing

A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters. While hypothesis testing was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710), followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth; see .

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