Multiple Hypothesis Testing

Related lectures (32)

Introduces descriptive statistics, hypothesis testing, p-values, and confidence intervals, emphasizing their importance in data analysis.

Covers hypothesis testing, Q-Q plots, and non-parametric tests in statistics.

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Covers data analysis techniques, statistical concepts, and outlier detection methods in depth.

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Explores confidence intervals, hypothesis testing, and decision-making using test statistics and p-values.

Covers statistical hypothesis testing, likelihood estimation, and confidence intervals construction.

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

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.

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

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

Explores GLM II, covering hypothesis testing, F-tests, multiple comparisons, and enriching the model to account for imaging artifacts.

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