이 강좌에 대하여: 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!
이 수업의 대상: 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.
강사: Daniel Lakens, Associate Professor
Department of Human-Technology Interaction
| 레벨 | 중급 |
| 약정 | 7 weeks of study, 3 hours a week |
| 언어 | 영어, 자막: 중국어 (간체자) |
| 통과 방법 | 강좌를 수료하려면 모든 평가 과제를 통과하십시오. |
| 사용자 등급 |
- Reading: Structure of the Course
- Reading: Passing the Course
- Reading: Research on Quizzes
- 연습 문제: Consent Form for Use of Data
- 연습 문제: Pop Quiz!
- Reading: Week 1: Overview
- 비디오: Frequentism, Likelihoods, Bayesian statistics
- 비디오: What is a p-value
- 비디오: Type 1 and Type 2 errors
- Reading: Assignment 1: Which p-values can you expect?
- 연습 문제: Answer Form Assignment 1 : Which p-values can you expect?
- 연습 문제: Pop Quiz 2!
- Reading: Week 2: Overview
- Reading: Interview with Professor Zoltan Dienes
- 비디오: Likelihoods
- Reading: Assignment 2.1: Likelihoods
- 연습 문제: Answer Form Assignment 2.1
- 비디오: Binomial Bayesian Inference
- Reading: Assignment 2.2: Bayesian Statistics
- 연습 문제: Answer Form Assignment 2.2: Bayesian Statistics
- 비디오: Bayesian Thinking
- 연습 문제: Pop Quiz 3!
- Reading: Week 3: Overview
- 비디오: Type 2 error control
- Reading: Assignment 3.1: Positive Predictive Value
- 연습 문제: Answer Form Assignment 3.1: Positive Predictive Value
- Reading: Assignment 3.2: Optional Stopping
- 연습 문제: Answer Form Assignment 3.2: Optional Stopping
- Reading: Interview Professor Dan Simons
- 비디오: Interview Professor Dan Simons
- 비디오: Pre-registration
- Reading: Week 4: Overview
- 비디오: Cohen's d
- 비디오: Correlations
- Reading: Assignment 4: Calculating Effect Sizes
- 연습 문제: Answer Form Assignment 4: Effect Sizes
- 연습 문제: Pop Quiz 4!
- Reading: Week 5: Overview
- Reading: Assignment 5.1: Confidence Intervals
- 연습 문제: Answer Form Assignment 5.1: Confidence Intervals and Capture Percentages
- 비디오: Sample Size Justification
- Reading: Assignment 5.2: Random Variation and Power Analysis
- 연습 문제: Answer Form Assignment 5.2: Random Variation and Power Analysis
- 비디오: P-Curve Analysis
- 연습 문제: Pop Quiz 5!
- Reading: Week 6: Overview
- 비디오: The Null is Always False
- Reading: Assignment 6: Equivalence Testing
- 연습 문제: Answer Form Assignment 6: Equivalence Testing
- 비디오: Theory Construction
- Reading: Week 7: Overview
- 비디오: Publication Bias
- 비디오: Open Science
- 연습 문제: Pop Quiz 6!
- 연습 문제: Practice Exam
각 강좌는 대화형 교과서 형식으로 이루어지며, 사전 녹화된 비디오, 테스트, 프로젝트를 포함합니다.
수천 명의 다른 동료 학습자와 함께 논의하고, 강좌 자료에 대하여 토론하고, 개념 습득을 위해 서로 도움을 받으십시오.
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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.
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)
AJ
Good pacing and ratio of exercises/lecture. I found the assignments very useful and the instructions easy to follow. Comparing my performance on the pre-tests and pop quizzes at the beginning of the course to those at the end clearly demonstrates that the coursework honed my stats intuition, and I'm very grateful! The only critical feedback I have is that occasionally, I found the wording of test/quiz questions to be a bit confusing. Thanks!