Acerca de este curso: 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 10.000 learners have enrolled so far!
Para quién es esta clase: This course is aimed at anyone who wants to improve their statistical inferences, either because you are preparing to do empirical research for the first time, or because you were never taught these important statistical concepts in a clear and accessible manner in the past. I didn't know most of the things you will learn in this course until well after I got my PhD, and I've tried to create the course I would have liked to have gotten when I started to do research. You should have some basic knowledge about calculating descriptive statistics, and how to perform t-tests, correlations, and ANOVA's (If you don't have this knowledge, try https://www.coursera.org/learn/basic-statistics first). We will use R in many of the assignments, but you don't need any previous knowledge of R - we will mainly use it as a fancy calculator.
Enseñado por: Daniel Lakens, Associate Professor
Department of Human-Technology Interaction
| Nivel | Intermediate |
| Compromiso | 7 weeks of study, 3 hours a week |
| Idioma | English, Subtítulos: Chinese (Simplified) |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
- Reading: Structure of the Course
- Reading: Passing the Course
- Reading: Research on Quizzes
- Practice Quiz: Consent Form for Use of Data
- Practice Quiz: Pop Quiz!
- Reading: Week 1: Overview
- Vídeo: Frequentism, Likelihoods, Bayesian statistics
- Vídeo: What is a p-value
- Vídeo: Type 1 and Type 2 errors
- Reading: Assignment 1: Which p-values can you expect?
- Practice Quiz: Answer Form Assignment 1 : Which p-values can you expect?
- Practice Quiz: Pop Quiz 2!
- Reading: Week 2: Overview
- Reading: Interview with Professor Zoltan Dienes
- Vídeo: Likelihoods
- Reading: Assignment 2.1: Likelihoods
- Practice Quiz: Answer Form Assignment 2.1
- Vídeo: Binomial Bayesian Inference
- Reading: Assignment 2.2: Bayesian Statistics
- Practice Quiz: Answer Form Assignment 2.2: Bayesian Statistics
- Vídeo: Bayesian Thinking
- Practice Quiz: Pop Quiz 3!
- Reading: Week 3: Overview
- Vídeo: Type 2 error control
- Reading: Assignment 3.1: Positive Predictive Value
- Practice Quiz: Answer Form Assignment 3.1: Positive Predictive Value
- Reading: Assignment 3.2: Optional Stopping
- Practice Quiz: Answer Form Assignment 3.2: Optional Stopping
- Reading: Interview Professor Dan Simons
- Vídeo: Interview Professor Dan Simons
- Vídeo: Pre-registration
- Reading: Week 4: Overview
- Vídeo: Cohen's d
- Vídeo: Correlations
- Reading: Assignment 4: Calculating Effect Sizes
- Practice Quiz: Answer Form Assignment 4: Effect Sizes
- Practice Quiz: Pop Quiz 4!
- Reading: Week 5: Overview
- Reading: Assignment 5.1: Confidence Intervals
- Practice Quiz: Answer Form Assignment 5.1: Confidence Intervals and Capture Percentages
- Vídeo: Sample Size Justification
- Reading: Assignment 5.2: Random Variation and Power Analysis
- Practice Quiz: Answer Form Assignment 5.2: Random Variation and Power Analysis
- Vídeo: P-Curve Analysis
- Practice Quiz: Pop Quiz 5!
- Reading: Week 6: Overview
- Vídeo: The Null is Always False
- Reading: Assignment 6: Equivalence Testing
- Practice Quiz: Answer Form Assignment 6: Equivalence Testing
- Vídeo: Theory Construction
- Reading: Week 7: Overview
- Vídeo: Publication Bias
- Vídeo: Open Science
- Practice Quiz: Pop Quiz 6!
- Practice Quiz: Practice Exam
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Questions are sometimes hard to understand if you are not a native speaker of the English language
I found out about this course as i stumbled across Professor Daniel's blog one day and i feel very lucky that i did. Chances are, like me, you are making some very common mistakes in using and interpreting statistics which is why this course is a MUST for anyone in a discipline that uses statistics and i wholeheartedly recommend it to anyone who has taken a few introductory courses on the subject, regardless of their level of expertise.
The instructor goes through very real and practical topics in the use of statistics and weaves it with adequate theory, examples through simulations, exercises and plenty of additional sources. Common mistakes are highlighted and very useful solutions/tips are provided. The level of difficulty is very accessible and there is not much mathematics beyond algebra and basic probability, although you can go more in depth into technical supplementary readings, should you choose to do so. The instructor also replied to queries and helped out where he could. There is also a really good corresponding (although independent) facebook group on methodology that is very informative and from which i learn new things everyday.
This course is one of the main reasons i am now learning more mathematics so i can properly use statistics in my field of study (Psychology) and i would like to thank professor Daniel for making such a wonderful, eye opening resource for everyone who uses statistics.
Enroll as soon as you can!
Sooo good! Cant even begin to explain how essential and wonderful this understanding is!
Great thanks to Dr Daniel! Such an expert in the field!
Thank you Dr!
very good course! The teaching style is good and the assignment in R is very helpful for me to understand the main ideas of this course.