Login to filter by course Login to filter by course Reset
Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.
Explores hypothesis testing in statistics, focusing on decision-making based on sample data and controlling error probabilities.
Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.
Introduces the t-test for assessing categorical effects on quantitative outcomes, covering hypothesis testing, assumptions, and alternative tests.
Explores t-tests, confidence intervals, ANOVA, and hypothesis testing in statistics, emphasizing the importance of avoiding false discoveries and understanding the logic behind statistical tests.
