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Related lectures (30)

Page 1 of 3

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

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

Observational Studies: Pitfalls and Solutions

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

Designing Experiments and Measuring Learning

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

Simpson's Paradox: Understanding and Randomized Trials

Explores Simpson's Paradox with a real-world example and emphasizes the significance of randomized trials.

Probability Model Construction

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

Sampling: Inference and Statistics

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

Natural Language Generation: Decoding & Training

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