T-tests and Empirical Tests

Related lectures (30)

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Explores likelihood ratio tests and the Neyman-Pearson Lemma for statistical hypothesis testing.

Covers basic probability theory, signal detection theory, statistics, and meta-statistics, explaining effect sizes, power, and hypothesis testing.

Explores hypothesis testing methodology using t-tests and the randomization test.

Covers statistical hypothesis testing, confidence intervals, p-values, and significance levels in hypothesis testing.

Explores sampling, inferential statistics, and effective experimentation in statistics.

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

Covers statistical modeling, estimation, CLT, confidence intervals, and hypothesis testing steps with practical examples.

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

Covers the fundamental concepts of statistics and their application in data science.

Covers the theory of sampling, focusing on statistics for mathematicians.