Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Concept

Selection bias

Related lectures (31)

Designing Experiments and Measuring Learning

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

Understanding Statistics & Experimental Design

Explores unequal variances, replication, power, effect size, biases, and their impact on research outcomes.

Introduction to Human Rights

Introduces key human rights concepts, explores discrimination, social norms, and the impact of algorithms on rights.

Red Influence: Attractiveness, Desirability, and Status

Explores the effects of red on attractiveness, desirability, and status, emphasizing statistical analysis and the challenges of replication and publication bias.

Meta-Analysis in Scientific Teaching

Explores the role of interaction in teaching and the strengths and weaknesses of meta-analysis in scientific research.

Data Issues in Research

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.

Fairness in Algorithmic Decision-Making

Delves into the challenges of ensuring fairness in algorithmic decision-making processes, addressing biases and historical injustices in data.

Observational vs Experimental Studies

Covers the reliability of observational and experimental studies, participant selection, data bias, and result accuracy.

Estimation Methods: BLP and Control-Function

Covers the inconsistency in estimates due to endogeneity and introduces the BLP and Control-Function estimation methods.

Natural Language Generation: Decoding & Training

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Bayesian Parameter Estimation

Covers an example of Bayesian parameter estimation and the trade-off between bias and variance in supervised learning.

Data Governance in Urban Settings

Explores data quality, biases in data, and the importance of good governance practices in urban data settings.

Faster Gaze Prediction: Dense Networks and Fisher Pruning

Explores the development of faster architectures for saliency prediction using deep networks and pruning methods.

AI and FX Practical Course

Introduces the AI and FX practical course, focusing on weekly readings and discussions on ethical and legal aspects of AI.

Sampling: conditional maximum likelihood estimation MOOC: Selected Topics on Discrete Choice

Covers Conditional Maximum Likelihood estimation, contribution to likelihood, and MEV model application in choice-based samples.

AI Ethics: Bias, Responsibility, and Accountability

Explores AI ethics, addressing bias, responsibility, and accountability in algorithm design and outcomes.

Take Home Exam Preparation

Covers the preparation for a take-home exam, focusing on analyzing case studies and identifying relevant concepts for assessment.

Natural Language Generation: Decoding Techniques and Training Challenges

Covers decoding methods and training challenges in natural language generation.

Effective Teaching Evaluations: Dimensions and Formulation Tips

Discusses the multidimensional nature of teaching evaluations and offers tips for formulating effective questions.

Statistics for Data Science: Introduction to Statistical Methods

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