Measuring Learning: Experimental Design

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Related lectures (32)

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Understanding Statistics & Experimental Design

Covers basic probability theory, ANOVA, experimental design, and correlations, emphasizing the importance of planning multiple tests and power analysis.

Observational Studies: Pitfalls and Solutions

Covers the pitfalls of observational studies, solutions to avoid biases, and the importance of valid conclusions from 'found data'.

Design of Experiments Basics

Covers the fundamentals of Design of Experiments (DOE) and experimental strategies.

Ethics in NLP

Discusses the ethical implications of NLP systems, focusing on biases, toxicity, and privacy concerns in language models.

Environmental Psychology: Human Interaction with the Environment

Explores human-environment interaction, cognitive biases, and barriers to climate change mitigation.

Full Factorial Design Introduction

Covers the basics of full factorial design, coding, graphical outputs, and data analysis.

DOE Qualitative factors III

Explores qualitative factors in Design of Experiments, including Latin squares, factorial designs, and ANOVA tables.

Understanding Statistics & Experimental Design

Covers basic probability theory, ANOVA, experimental design, and statistical reporting errors.

Causal Analysis of Observational Data

Covers causal analysis of observational data, pitfalls, tools for valid conclusions, and addressing confounding variables.

Cognition, Language & Emotions

Explores cognitive biases, emotions, and language evolution, covering memory failures, decision-making heuristics, and the origins of language.