Course Summary
This course focuses on improving the quality of statistical questions and providing better answers to those questions. Students will learn how to properly frame statistical questions, collect and analyze data, and draw valid conclusions.Key Learning Points
- Learn how to frame statistical questions in a meaningful way
- Understand the importance of data collection and analysis
- Develop skills in drawing valid conclusions from statistical data
Job Positions & Salaries of people who have taken this course might have
- USA: $60,000 - $120,000
- India: ₹300,000 - ₹1,200,000
- Spain: €25,000 - €50,000
- USA: $60,000 - $120,000
- India: ₹300,000 - ₹1,200,000
- Spain: €25,000 - €50,000
- USA: $45,000 - $90,000
- India: ₹300,000 - ₹1,000,000
- Spain: €20,000 - €35,000
- USA: $60,000 - $120,000
- India: ₹300,000 - ₹1,200,000
- Spain: €25,000 - €50,000
- USA: $45,000 - $90,000
- India: ₹300,000 - ₹1,000,000
- Spain: €20,000 - €35,000
- USA: $70,000 - $130,000
- India: ₹400,000 - ₹1,500,000
- Spain: €30,000 - €55,000
Related Topics for further study
- Statistical Question Framing
- Data Collection and Analysis
- Drawing Valid Conclusions
- Statistical Software
- Applied Statistics
Learning Outcomes
- Develop the ability to frame statistical questions in a meaningful way
- Gain proficiency in data collection and analysis
- Acquire skills in drawing valid conclusions from statistical data
Prerequisites or good to have knowledge before taking this course
- Basic statistics knowledge
- Familiarity with statistical software
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Interactive quizzes
Similar Courses
- Introduction to Probability and Data
- Data Analysis and Statistical Inference
Related Education Paths
Related Books
Description
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.
Knowledge
- Ask better questions in empirical research
- Design more informative studies
- Evaluate the scientific literature taking bias into account
- Reflect on current norms, and how you can improve your research practices
Outline
- 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
Summary of User Reviews
Improving Statistical Questions is a highly rated course on Coursera that teaches students how to create better statistical questions. Many users appreciated the practical nature of the course and found it to be a great resource for improving their data analysis skills.Key Aspect Users Liked About This Course
The practical nature of the coursePros from User Reviews
- The course content is well-structured and easy to follow
- The instructors are knowledgeable and provide helpful feedback
- The course provides real-world examples and practical exercises
- The course is suitable for both beginners and advanced learners
- The course is a great value for the price
Cons from User Reviews
- Some users found the course to be too basic and not challenging enough
- A few users reported technical issues with the online platform
- Some users felt that the course lacked depth and could have covered more advanced topics
- The course requires a significant time commitment to complete
- Some users found the assessments to be too difficult
Daniel LakensTop Instructor
4.9 Raiting
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