Module 1: Improving Your Statistical Questions-Course Introduction (Read before Starting)
Download Course Materials and Course Structure (Must Read)
Module 1: Improving Your Statistical Questions-Lecture 1.1: Improving Your Statistical Questions
Lecture 1.1: Improving Your Statistical Questions
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Module 1: Improving Your Statistical Questions-Lecture 1.2: Do You Really Want to Test a Hypothesis?
Lecture 1.2: Do You Really Want to Test a Hypothesis?
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Module 1: Improving Your Statistical Questions-Lecture 1.3: Risky Predictions
Lecture 1.3: Risky Predictions
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Module 1: Improving Your Statistical Questions-Assignment 1.1: Testing Range Predictions
Assignment 1.1: Testing Range Predictions
Module 2: Falsifying Predictions-Lecture 2.1: Falsifying predictions in theory
Lecture 2.1: Falsifying Predictions in Theory
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Module 2: Falsifying Predictions-Lecture 2.2: Setting the Smallest Effect Size Of Interest
Lecture 2.2: Setting the Smallest Effect Size Of Interest
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Module 2: Falsifying Predictions-Assignment 2.1: The Small Telescopes Approach to Setting a SESOI
Assignment 2.1: The Small Telescopes Approach to Setting a SESOI
Module 2: Falsifying Predictions-Assignment 2.2: Setting the SESOI Based on Resources
Assignment 2.2: Setting the SESOI Based on Resources
Module 2: Falsifying Predictions-Lecture 2.3: Falsifying Predictions in Practice
Lecture 2.3: Falsifying Predictions in Practice
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Module 2: Falsifying Predictions-Assignment 2.3: Equivalence Testing
Assignment 2.3: Equivalence Testing
Module 3: Designing Informative Studies-Lecture 3.1: Justifying Error Rates
Lecture 3.1: Justifying Error Rates
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Module 3: Designing Informative Studies-Assignment 3.1: Confidence Intervals for Standard Deviations
Assignment 3.1: Confidence Intervals for Standard Deviations
Module 3: Designing Informative Studies-Lecture 3.2: Power Analysis
Lecture 3.2: Power Analysis
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Module 3: Designing Informative Studies-Assignment 3.2: Power Analysis for ANOVA Designs
Assignment 3.2: Power Analysis for ANOVA Designs
Module 3: Designing Informative Studies-Lecture 3.3: Simulation
Lecture 3.3: Simulation
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Module 4: Meta-Analysis and Bias Detection-Lecture 4.1: Mixed Results
Lecture 4.1: Mixed Results
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Module 4: Meta-Analysis and Bias Detection-Assignment 4.1: Likelihood of Significant Findings
Assignment 4.1: Likelihood of Significant Findings
Module 4: Meta-Analysis and Bias Detection-Lecture 4.2: Intro to Meta-Analysis
Lecture 4.2: Intro to Meta-Analysis
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Module 4: Meta-Analysis and Bias Detection-Assignment 4.2: Introduction to Meta-Analysis
Assignment 4.2: Introduction to Meta-Analysis
Module 4: Meta-Analysis and Bias Detection-Lecture 4.3: Bias Detection
Lecture 4.3: Bias Detection
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Module 4: Meta-Analysis and Bias Detection-Assignment 4.3: Detecting Publication Bias
Assignment 4.3: Detecting Publication Bias
Module 4: Meta-Analysis and Bias Detection-Assignment 4.4: Checking Your Stats
Assignment 4.4: Checking Your Stats
Module 5: Computational Reproducibility, Philosophy of Science, and Scientific Integrity-Lecture 5.1: Computational Reproducibility
Lecture 5.1: Computational Reproducibility
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Module 5: Computational Reproducibility, Philosophy of Science, and Scientific Integrity-Assignment 5.1: Computational Reproducibility
Assignment 5.1: Computational Reproducibility
Module 5: Computational Reproducibility, Philosophy of Science, and Scientific Integrity-Lecture 5.2: Philosophy of Science in Practice
Lecture 5.2: Philosophy of Science in Practice
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Module 5: Computational Reproducibility, Philosophy of Science, and Scientific Integrity-Assignment 5.2: Does Your Philosophy of Science Matter in Practice?
Assignment 5.2: Does Your Philosophy of Science Matter in Practice?
Module 5: Computational Reproducibility, Philosophy of Science, and Scientific Integrity-Lecture 5.3: Scientific Integrity in Practice
Lecture 5.3: Scientific Integrity in Practice
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