TU/e - Improving your statistical inferences
- Offered byCoursera
Improving your statistical inferences at Coursera Overview
Duration | 28 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 inferences at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 28 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, German, Russian, English, Spanish
Improving your statistical inferences at Coursera Course details
- This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles.
- In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. We will experience the problems with optional stopping and learn how to prevent these problems by using sequential analyses. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses. Finally, you will learn how to examine whether the null hypothesis is true using equivalence testing and Bayesian statistics, and how to pre-register a study, and share your data on the Open Science Framework.
- All videos now have Chinese subtitles. More than 30.000 learners have enrolled so far!
- If you enjoyed this course, I can recommend following it up with me new course "Improving Your Statistical Questions"
Improving your statistical inferences at Coursera Curriculum
Introduction + Frequentist Statistics
Introduction
Frequentism, Likelihoods, Bayesian statistics
What is a p-value
Type 1 and Type 2 errors
Structure of the Course
Passing the Course
Research on Quizzes
Week 1: Overview
Assignment 1: Which p-values can you expect?
Consent Form for Use of Data
Pop Quiz!
Answer Form Assignment 1 : Which p-values can you expect?
Pop Quiz 2!
Exam Week 1
Likelihoods & Bayesian Statistics
Interview: Zoltan Dienes
Likelihoods
Binomial Bayesian Inference
Bayesian Thinking
Week 2: Overview
Interview with Professor Zoltan Dienes
Assignment 2.1: Likelihoods
Assignment 2.2: Bayesian Statistics
Answer Form Assignment 2.1
Answer Form Assignment 2.2: Bayesian Statistics
Pop Quiz 3!
Exam Week 2
Multiple Comparisons, Statistical Power, Pre-Registration
Type 1 error control
Type 2 error control
Interview Professor Dan Simons
Pre-registration
Week 3: Overview
Assignment 3.1: Positive Predictive Value
Assignment 3.2: Optional Stopping
Interview Professor Dan Simons
Answer Form Assignment 3.1: Positive Predictive Value
Answer Form Assignment 3.2: Optional Stopping
Exam Week 3
Effect Sizes
Effect Sizes
Cohen's d
Correlations
Week 4: Overview
Assignment 4: Calculating Effect Sizes
Answer Form Assignment 4: Effect Sizes
Pop Quiz 4!
Exam Week 4
Confidence Intervals, Sample Size Justification, P-Curve analysis
Confidence Intervals
Sample Size Justification
P-Curve Analysis
Week 5: Overview
Assignment 5.1: Confidence Intervals
Assignment 5.2: Random Variation and Power Analysis
Answer Form Assignment 5.1: Confidence Intervals and Capture Percentages
Answer Form Assignment 5.2: Random Variation and Power Analysis
Pop Quiz 5!
Exam Week 5
Philosophy of Science & Theory
Philosophy of Science
The Null is Always False
Theory Construction
Week 6: Overview
Assignment 6: Equivalence Testing
Answer Form Assignment 6: Equivalence Testing
Exam Week 6
Open Science
Replications
Publication Bias
Open Science
Week 7: Overview
Final Exam
Pop Quiz 6!
Practice Exam
Graded Final Exam
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