Informações sobre o 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 quem é direcionado este curso: 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.
Ministrado por: Daniel Lakens, Associate Professor
Department of Human-Technology Interaction
| Nível | Intermediário |
| Compromisso | 7 weeks of study, 3 hours a week |
| Idioma | English, Legendas: Chinese (Simplified) |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
- Reading: Structure of the Course
- Reading: Passing the Course
- Reading: Research on Quizzes
- Cuestionario de práctica: Consent Form for Use of Data
- Cuestionario de práctica: 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?
- Cuestionario de práctica: Answer Form Assignment 1 : Which p-values can you expect?
- Cuestionario de práctica: Pop Quiz 2!
- Reading: Week 2: Overview
- Reading: Interview with Professor Zoltan Dienes
- Vídeo: Likelihoods
- Reading: Assignment 2.1: Likelihoods
- Cuestionario de práctica: Answer Form Assignment 2.1
- Vídeo: Binomial Bayesian Inference
- Reading: Assignment 2.2: Bayesian Statistics
- Cuestionario de práctica: Answer Form Assignment 2.2: Bayesian Statistics
- Vídeo: Bayesian Thinking
- Cuestionario de práctica: Pop Quiz 3!
- Reading: Week 3: Overview
- Vídeo: Type 2 error control
- Reading: Assignment 3.1: Positive Predictive Value
- Cuestionario de práctica: Answer Form Assignment 3.1: Positive Predictive Value
- Reading: Assignment 3.2: Optional Stopping
- Cuestionario de práctica: 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
- Cuestionario de práctica: Answer Form Assignment 4: Effect Sizes
- Cuestionario de práctica: Pop Quiz 4!
- Reading: Week 5: Overview
- Reading: Assignment 5.1: Confidence Intervals
- Cuestionario de práctica: Answer Form Assignment 5.1: Confidence Intervals and Capture Percentages
- Vídeo: Sample Size Justification
- Reading: Assignment 5.2: Random Variation and Power Analysis
- Cuestionario de práctica: Answer Form Assignment 5.2: Random Variation and Power Analysis
- Vídeo: P-Curve Analysis
- Cuestionario de práctica: Pop Quiz 5!
- Reading: Week 6: Overview
- Vídeo: The Null is Always False
- Reading: Assignment 6: Equivalence Testing
- Cuestionario de práctica: Answer Form Assignment 6: Equivalence Testing
- Vídeo: Theory Construction
- Reading: Week 7: Overview
- Vídeo: Publication Bias
- Vídeo: Open Science
- Cuestionario de práctica: Pop Quiz 6!
- Cuestionario de práctica: Practice Exam
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
Nicely packed body of information necessary to understand your data and to infer any judgements about real world impact of scientific research. The course led me to question my way of creating inferences about my research and conclusions of others. Now, I can be more precise in formulating hypotheses and interpreting results in the way that is closer to truth. Thank you.
Great course! Highly recommended.
One thing to improve - I would like to see more theory behind the different effect sizes (eta-squared/omega squared/etc)
A great course to learn or refresh theoretical concepts behind statistical inferences. There is also a lot of hands-on material and additional content. I think I will come back to the videos and slides when I want to refresh some concepts.
I find this course very useful, since these are topics that do not stick when you are completely new to statics, but are very useful once you have few years experience in practice. My only remark is that sometimes the multiple choice answers in the quizzes were not clear enough, so a bit confusing.