Statistical significance

Formal sciences
Statistics
Statistical inference
Statistical hypothesis testing

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Related lectures (32)

Page 3 of 4

Pitfalls in Empirical NLP Research

Explores pitfalls in NLP research, emphasizing the importance of standardized metrics and statistical testing.

Statistics: Hypothesis Testing & Confidence Intervals

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

Interval Estimation

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

Two-Sample T-Test

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

Property Testing: Uniform Distribution

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

Ionisation and Discharges

Covers ionization, discharges, energy transfer, and star creation on Earth.

Explainable Neural Networks

Explores the concept of explainable neural networks and their significance in improving model interpretability, particularly in finance and house price valuation.

Hypothesis Testing in Statistics

Explores hypothesis testing, significance levels, errors, GWAS, optimal testing, and point estimation in statistics.

Statistical Significance: Maximum Likelihood Estimation and Confidence Intervals

Explores type I and type II errors, critical values, and confidence intervals in statistical significance.

Radiation Detection: From Signal to Observables

Explores gaseous, scintillator, and semiconductor detectors, counting statistics, and statistical tests for radiation detection.