Lectures related to Observational Studies: Pitfalls and Solutions

Observational Studies: Pitfalls and Valid Conclusions

Highlights the pitfalls of observational studies and the importance of randomized experiments.

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

Observational Studies: Pitfalls and Solutions

Explores observational studies, pitfalls, solutions, and the importance of valid conclusions in research projects.

Observational Studies: Pitfalls and Solutions

Explores the challenges of observational studies, emphasizing the importance of randomization and sensitivity analysis in drawing valid conclusions from 'found data'.

Protein Mass Spectrometry and Proteomics: Randomization Overview

Explores the significance of randomization in protein mass spectrometry and proteomics, highlighting its role in minimizing bias and ensuring research validity.

Causal Inference: Estimands and Ontologies

Explores causal inference, emphasizing the importance of committing to an ontology for drawing causal inferences and selecting appropriate estimands.

Evaluating Learning Technologies: Randomized Experiments Insights

Discusses the impact of live streaming lectures on student performance and attendance, revealing varied effects based on student ability levels.

Statistical Modeling: Fundamentals and Process Factors

Covers the fundamental concept of statistical modeling and process factors for prediction and optimization.

Design of Experiments Basics

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

Design of Experiments: Weighing Strategies and Causality

Explores statistics, experimental design, weighing strategies, and causality in experiments.

Introduction to Probability Theory

Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

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Designing Experiments and Measuring Learning

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

Probability Model Construction

Explores constructing a probability model, random sampling, variance calculation, and allocation optimization in experiments.

Probability and Statistics

Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.

Ensemble Fondamental: Events Space

Discusses the fundamental set and the events space with illustrative examples.

Experimental Design in Biostatistics

Introduces experimental design in biostatistics, covering research process, hypothesis testing, ANOVA modeling, and interpretation of results.

Experimental Design: Replication, Randomization, Blocking

Delves into experimental design in genomics, emphasizing replication, randomization, and blocking for reducing bias and controlling variation.

Probability and Statistics

Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.