From signal to observables

Related lectures (31)

Page 3 of 4

Explores hypothesis testing in statistics, focusing on decision-making based on sample data and controlling error probabilities.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Explores confidence intervals, hypothesis testing, and ROC curves in statistical analysis.

Covers property testing for uniform distributions and the importance of reliable predictions.

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

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

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Explains the two-sample t-test for comparing means of independent samples, including hypothesis testing steps and test statistic calculation.

Explores the influence of language on headline success through real-world data analysis and statistical testing.

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