Improving your statistical inferences

About this course: 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!

Who is this class for: 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.

Created by:  Eindhoven University of Technology

• Taught by:  Daniel Lakens, Associate Professor

  Department of Human-Technology Interaction

Level Intermediate Commitment 7 weeks of study, 3 hours a week Language English, Subtitles: Chinese (Simplified) How To Pass Pass all graded assignments to complete the course. User Ratings 4.9 stars Average User Rating 4.9See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Eindhoven University of Technology Eindhoven University of Technology (TU/e) is a research-driven, design-oriented university of technology with a strong international focus. The university was founded in 1956, and has around 8,500 students and 3,000 staff. TU/e has defined strategic areas focusing on the societal challenges in Energy, Health and Smart Mobility. The Brainport Eindhoven region is one of world’s smartest; it won the title Intelligent Community of the Year 2011.

Syllabus


WEEK 1


Introduction + Frequentist Statistics



4 videos, 5 readings, 2 practice quizzes expand

1. Video: Introduction
2. Reading: Structure of the Course
3. Reading: Passing the Course
4. Reading: Research on Quizzes
5. Practice Quiz: Consent Form for Use of Data
6. Reading: Week 1: Overview
7. Video: Frequentism, Likelihoods, Bayesian statistics
8. Video: What is a p-value
9. Video: Type 1 and Type 2 errors
10. Reading: Assignment 1: Which p-values can you expect?
11. Practice Quiz: Answer Form Assignment 1 : Which p-values can you expect?

Graded: Exam Week 1

WEEK 2


Likelihoods & Bayesian Statistics



4 videos, 4 readings, 2 practice quizzes expand

1. Reading: Week 2: Overview
2. Reading: Interview with Professor Zoltan Dienes
3. Video: Interview: Zoltan Dienes
4. Video: Likelihoods
5. Reading: Assignment 2.1: Likelihoods
6. Practice Quiz: Answer Form Assignment 2.1
7. Video: Binomial Bayesian Inference
8. Reading: Assignment 2.2: Bayesian Statistics
9. Practice Quiz: Answer Form Assignment 2.2: Bayesian Statistics
10. Video: Bayesian Thinking

Graded: Exam Week 2

WEEK 3


Multiple Comparisons, Statistical Power, Pre-Registration



4 videos, 4 readings, 2 practice quizzes expand

1. Reading: Week 3: Overview
2. Video: Type 1 error control
3. Video: Type 2 error control
4. Reading: Assignment 3.1: Positive Predictive Value
5. Practice Quiz: Answer Form Assignment 3.1: Positive Predictive Value
6. Reading: Assignment 3.2: Optional Stopping
7. Practice Quiz: Answer Form Assignment 3.2: Optional Stopping
8. Reading: Interview Professor Dan Simons
9. Video: Interview Professor Dan Simons
10. Video: Pre-registration

Graded: Exam Week 3

WEEK 4


Effect Sizes



3 videos, 2 readings, 1 practice quiz expand

1. Reading: Week 4: Overview
2. Video: Effect Sizes
3. Video: Cohen's d
4. Video: Correlations
5. Reading: Assignment 4: Calculating Effect Sizes
6. Practice Quiz: Answer Form Assignment 4: Effect Sizes

Graded: Exam Week 4

WEEK 5


Confidence Intervals, Sample Size Justification, P-Curve analysis



3 videos, 3 readings, 2 practice quizzes expand

1. Reading: Week 5: Overview
2. Video: Confidence Intervals
3. Reading: Assignment 5.1: Confidence Intervals
4. Practice Quiz: Answer Form Assignment 5.1: Confidence Intervals and Capture Percentages
5. Video: Sample Size Justification
6. Reading: Assignment 5.2: Random Variation and Power Analysis
7. Practice Quiz: Answer Form Assignment 5.2: Random Variation and Power Analysis
8. Video: P-Curve Analysis

Graded: Exam Week 5

WEEK 6


Philosophy of Science & Theory



3 videos, 2 readings, 1 practice quiz expand

1. Reading: Week 6: Overview
2. Video: Philosophy of Science
3. Video: The Null is Always False
4. Reading: Assignment 6: Equivalence Testing
5. Practice Quiz: Answer Form Assignment 6: Equivalence Testing
6. Video: Theory Construction

Graded: Exam Week 6

WEEK 7


Open Science



3 videos, 1 reading expand

1. Reading: Week 7: Overview
2. Video: Replications
3. Video: Publication Bias
4. Video: Open Science

Graded: Assignment 7: Open Science

WEEK 8


Final Exam
This module contains a practice exam and a graded exam. Both quizzes cover content from the entire course. We recommend making these exams only after you went through all the other modules.


1 practice quiz expand

1. Practice Quiz: Practice Exam

Graded: Graded Final Exam