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
- 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
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The course taught me a lot about data analysis and the philosophy of science. By focusing on the processes associated to doing science (data collection, theory generation, statistical inference) the course prepares you to design studies and think better about any area of research (it`s all data after all). But not just that, it made me rethink various things I do in life. I have to say that while, and now after, doing it I started to take a more scientific and data driven approach to all problems in my life. 10/10
Taking this course was the best decision of the start of my grad school. It has massively improved my ability to interpret other papers and plan my own experiments, as well as changing how I view psychology/science in general. Plus Daniel is a great teacher :)
Excellent course that changed my views on interpreting p-values, confidence intervals, etc. and will surely make my statistical inferences much better.
The course was great for refreshing my understanding of statistical inferences. Additionally, it provides an easy to understand introduction to bayesian thinking. The apps and websites, as well as the R-codes and excel-sheets provided alongside the assignments, and the lecture videos are of high quality and proof of a thorough and intesive preparation of the material. The material is very helpful, both for learners and for those teaching statistics to students. Plus, Professor Lakens lectures are entertaining and fun to watch.
I really enjoyed the course and have already recommended it to my department.