TU/e - Improving Your Statistical Questions
- Offered byCoursera
Improving Your Statistical Questions at Coursera Overview
Duration | 18 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Improving Your Statistical Questions at Coursera Highlights
- Earn a shareable certificate upon completion.
- Flexible deadlines according to your schedule.
- Earn a certificate from the Eindhoven University of Technology upon completion of course.
Improving Your Statistical Questions at Coursera Course details
- This course aims to help you to ask better statistical questions when performing empirical research. We will discuss how to design informative studies, both when your predictions are correct, as when your predictions are wrong. We will question norms, and reflect on how we can improve research practices to ask more interesting questions. In practical hands on assignments you will learn techniques and tools that can be immediately implemented in your own research, such as thinking about the smallest effect size you are interested in, justifying your sample size, evaluate findings in the literature while keeping publication bias into account, performing a meta-analysis, and making your analyses computationally reproducible.
- If you have the time, it is recommended that you complete my course 'Improving Your Statistical Inferences' before enrolling in this course, although this course is completely self-contained.
Improving Your Statistical Questions at Coursera Curriculum
Module 1: Improving Your Statistical Questions
Lecture 1.1: Improving Your Statistical Questions
Lecture 1.2: Do You Really Want to Test a Hypothesis?
Lecture 1.3: Risky Predictions
Download Course Materials and Course Structure (Must Read)
Assignment 1.1: Testing Range Predictions
Consent Form for Use of Data
Welcome: Short Survey
Answer Form Assignment 1.1: Testing Range Predictions
Module 2: Falsifying Predictions
Lecture 2.1: Falsifying Predictions in Theory
Lecture 2.2: Setting the Smallest Effect Size Of Interest
Lecture 2.3: Falsifying Predictions in Practice
Assignment 2.1: The Small Telescopes Approach to Setting a SESOI
Assignment 2.2: Setting the SESOI Based on Resources
Assignment 2.3: Equivalence Testing
Answer Form Assignment 2.1: The Small Telescopes Approach to Setting a SESOI
Answer Form Assignment 2.2: Setting the SESOI Based on Resources
Answer Form Assignment 2.3: Equivalence Testing
Module 3: Designing Informative Studies
Lecture 3.1: Justifying Error Rates
Lecture 3.2: Power Analysis
Lecture 3.3: Simulation
Assignment 3.1: Confidence Intervals for Standard Deviations
Assignment 3.2: Power Analysis for ANOVA Designs
Answer Form Assignment 3.1: Confidence Intervals for Standard Deviations
Answer Form Assignment 3.2: Power Analysis for ANOVA Designs
Module 4: Meta-Analysis and Bias Detection
Lecture 4.1: Mixed Results
Lecture 4.2: Intro to Meta-Analysis
Lecture 4.3: Bias Detection
Assignment 4.1: Likelihood of Significant Findings
Assignment 4.2: Introduction to Meta-Analysis
Assignment 4.3: Detecting Publication Bias
Assignment 4.4: Checking Your Stats
Answer Form Assignment 4.1: Likelihood of Significant Findings
Answer Form Assignment 4.2: Introduction to Meta-Analysis
Answer Form Assignment 4.3: Detecting Publication Bias
Module 5: Computational Reproducibility, Philosophy of Science, and Scientific Integrity
Lecture 5.1: Computational Reproducibility
Lecture 5.2: Philosophy of Science in Practice
Lecture 5.3: Scientific Integrity in Practice
Assignment 5.1: Computational Reproducibility
Assignment 5.2: Does Your Philosophy of Science Matter in Practice?
Module 6: Final Exam
Graded Final Exam
Improving Your Statistical Questions at Coursera Admission Process
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