Pitfalls in Empirical NLP Research

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Page 3 of 4

Provides an overview of basic probability theory, ANOVA, t-tests, central limit theorem, metrics, confidence intervals, and non-parametric tests.

Covers a homework assignment on data wrangling and analysis using Python's pandas library for real-world datasets.

Delves into NLP evaluation, covering gold standards, precision, recall, and statistical significance.

Covers neuronal morphologies, visualisation, measurements, and statistical tests.

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

Explores experimental design challenges in social sciences, emphasizing hypothesis formulation, variable control, and bias mitigation.

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

Explains the statistical analysis of Genome-wide Association Studies and the importance of adjusting for multiple testing.

Covers hypothesis testing, confidence intervals, and their applications in statistics.

Covers hypothesis testing, Neyman-Pearson lemma, ROC curves, and optimal tests with examples and simulations.