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Lecture
Hypothesis Testing in Statistics
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Related lectures (31)
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Descriptive Statistics: Hypothesis Testing
Introduces descriptive statistics, hypothesis testing, p-values, and confidence intervals, emphasizing their importance in data analysis.
Statistical Hypothesis Testing
Covers statistical hypothesis testing, confidence intervals, p-values, and significance levels in hypothesis testing.
Multiple Hypothesis Testing
Explores the challenges of multiple hypothesis testing and non-parametric estimation techniques.
Understanding Statistics & Experimental Design
Provides an overview of basic probability theory, ANOVA, t-tests, central limit theorem, metrics, confidence intervals, and non-parametric tests.
Likelihood Ratio Tests: Optimality and Applications
Explores the theory and applications of likelihood ratio tests in statistical hypothesis testing.
GLM: Statistical Hypothesis Testing
Explores statistical hypothesis testing, error types, thresholding, and multiple comparisons in GLM.
Describing Data: Statistics & Uncertainty
Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.
Statistical Hypothesis Testing: Unilateral and Bilateral Pairs
Explores unilateral and bilateral pairs in statistical hypothesis testing, covering critical values, test statistics, and p-values.
Hypothesis Testing: T-Test and Chi-Square Test
Explains hypothesis testing using T-test and Chi-square test to compare means and assess independence of variables.
Statistics & Experimental Design
Explores conditional probability, Framingham studies, effect size, t-test, and sampling error in statistics.
