Two-Sample T-Test

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Page 2 of 3

Covers the basics of t tests, including hypothesis testing, significance levels, P-values, Z-tests, and the t distribution.

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

Explains hypothesis testing using T-test and Chi-square test to compare means and assess independence of variables.

Explores unequal variances, replication, power, effect size, biases, and their impact on research outcomes.

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

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

Introduces hypothesis testing, focusing on the Z-test and critical regions.

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

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

Explores unilateral and bilateral pairs in statistical hypothesis testing, covering critical values, test statistics, and p-values.