Experimental Research Design: T-Test Analysis

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Explores hypothesis testing, emphasizing the state of nature and the importance of choosing the most powerful test.

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Explores t-tests, z-tests, and empirical tests for sample comparison and hypothesis testing.

Covers the calculation of paired t-tests, advantages/disadvantages of different t-tests, and the concept of ANOVA.

Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.

Explores conditional probability, Framingham studies, effect size, t-test, and sampling error in statistics.

Covers statistical hypothesis testing, confidence intervals, p-values, and significance levels in hypothesis testing.

Covers Analysis of Variance (ANOVA) for assessing categorical variable effects on quantitative outcomes.

Covers the fundamental concepts of statistics and their application in data science.

Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.