Lectures related to Design of Experiments: Weighing Strategies and Causality

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.

Observational Studies: Pitfalls and Valid Conclusions

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

Observational Studies: Pitfalls and Solutions

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

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'.

Causal Analysis of Observational Data

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

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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.

Differential Amplifier Design

Covers the design of a differential amplifier with key parameters like gain and bias current.

Sampling: Inference and Statistics

Explores sampling in inferential statistics, emphasizing the impact of sample size and randomness on inference accuracy.

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.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.

Fractional Factorial Designs

Explores fractional factorial designs, from types to analysis and model building.

Designing Experiments and Measuring Learning

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

Understanding Statistics & Experimental Design

Emphasizes the critical importance of understanding statistics to avoid errors in research and decision-making, using motivating examples and practical exercises.

Design of Experiments Basics

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

Understanding Statistics & Experimental Design

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

Introduction to Fractional Factorial Designs

Covers Fractional Factorial Designs, types, examples, balanced designs, and exploiting redundancies in interactions.

Understanding Statistics & Experimental Design

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

DOE Qualitative factors III

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