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Lecture
Hypothesis Testing: Neyman-Pearson Framework
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Related lectures (32)
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Statistical Hypothesis Testing
Covers statistical hypothesis testing, confidence intervals, p-values, and significance levels in hypothesis testing.
Descriptive Statistics: Hypothesis Testing
Introduces descriptive statistics, hypothesis testing, p-values, and confidence intervals, emphasizing their importance in data 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.
Describing Data: Statistics & Uncertainty
Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.
Detection & Estimation
Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.
Statistics & Experimental Design
Explores conditional probability, Framingham studies, effect size, t-test, and sampling error in statistics.
Hypothesis Testing in Statistics
Explores hypothesis testing in statistics, focusing on decision-making based on sample data and controlling error probabilities.
Hypothesis Testing: Statistics Overview
Provides an overview of hypothesis testing, p-values, Wald test, and non-parametric statistics.
Chi-Square Test: Independence Hypothesis
Explains the Chi-Square test for independence hypothesis and its practical applications.
Hypothesis Testing and Confidence Intervals: An Overview
Covers hypothesis testing, confidence intervals, and their applications in statistics.
