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Improving your statistical inferences
  • Level Professional
  • Duration 28 hours
  • Course by Eindhoven University of Technology
  • Offered by

About

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 30.000 learners have enrolled so far! If you enjoyed this course, I can recommend following it up with me new course "Improving Your Statistical Questions"

Modules

Course Introduction

    • 1 Videos
    • 3 Readings
    • 2 Assignment
Show info about module content

2 Assignment

  • Consent Form for Use of Data
  • Pop Quiz!

1 Videos

  • Introduction

3 Readings

  • Structure of the Course
  • Passing the Course
  • Research on Quizzes

Week 1: Overview

    • 1 Readings
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1 Readings

  • Week 1: Overview

Lecture 1.1

    • 1 Videos
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1 Videos

  • Frequentism, Likelihoods, Bayesian statistics

Lecture 1.2: What is a p-value?

    • 1 Videos
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1 Videos

  • What is a p-value

Lecture 1.3: Type 1 and Type 2 errors

    • 1 Videos
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1 Videos

  • Type 1 and Type 2 errors

Assignment 1: Which p-values can you expect?

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Answer Form Assignment 1 : Which p-values can you expect?

1 Readings

  • Assignment 1: Which p-values can you expect?

Exam Week 1

    • 2 Assignment
Show info about module content

2 Assignment

  • Pop Quiz 2!
  • Exam Week 1

Week 2: Overview

    • 1 Readings
Show info about module content

1 Readings

  • Week 2: Overview

Extra: Interview with Zoltan Dienes

    • 1 Videos
    • 1 Readings
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1 Videos

  • Interview: Zoltan Dienes

1 Readings

  • Interview with Professor Zoltan Dienes

Lecture 2.1

    • 1 Videos
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1 Videos

  • Likelihoods

Assignment 2.1: Likelihoods

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Answer Form Assignment 2.1

1 Readings

  • Assignment 2.1: Likelihoods

Lecture 2.2: Binomial Bayesian Inference

    • 1 Videos
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1 Videos

  • Binomial Bayesian Inference

Assignment 2.2: Bayesian Statistics

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Answer Form Assignment 2.2: Bayesian Statistics

1 Readings

  • Assignment 2.2: Bayesian Statistics

Lecture 2.3: Bayesian Thinking

    • 1 Videos
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1 Videos

  • Bayesian Thinking

Exam Week 2

    • 2 Assignment
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2 Assignment

  • Pop Quiz 3!
  • Exam Week 2

Week 3: Overview

    • 1 Readings
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1 Readings

  • Week 3: Overview

Lecture 3.1: Type 1 error control

    • 1 Videos
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1 Videos

  • Type 1 error control

Lecture 3.2: Type 2 error control

    • 1 Videos
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1 Videos

  • Type 2 error control

Assignment 3.1: Positive Predictive Value

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Answer Form Assignment 3.1: Positive Predictive Value

1 Readings

  • Assignment 3.1: Positive Predictive Value

Assignment 3.2: Optional Stopping

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Answer Form Assignment 3.2: Optional Stopping

1 Readings

  • Assignment 3.2: Optional Stopping

Extra: Interview with Professor Dan Simons

    • 1 Videos
    • 1 Readings
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1 Videos

  • Interview Professor Dan Simons

1 Readings

  • Interview Professor Dan Simons

Lecture 3.3: Pre-registration

    • 1 Videos
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1 Videos

  • Pre-registration

Exam Week 3

    • 1 Assignment
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1 Assignment

  • Exam Week 3

Week 4: Overview

    • 1 Readings
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1 Readings

  • Week 4: Overview

Lecture 4.1: Effect Sizes

    • 1 Videos
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1 Videos

  • Effect Sizes

Lecture 4.2: Cohen's d

    • 1 Videos
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1 Videos

  • Cohen's d

Lecture 4.3: Correlations

    • 1 Videos
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1 Videos

  • Correlations

Assignment 4: Calculating Effect Sizes

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Answer Form Assignment 4: Effect Sizes

1 Readings

  • Assignment 4: Calculating Effect Sizes

Exam Week 4

    • 2 Assignment
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2 Assignment

  • Pop Quiz 4!
  • Exam Week 4

Week 5: Overview

    • 1 Readings
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1 Readings

  • Week 5: Overview

Lecture 5.1: Confidence Intervals

    • 1 Videos
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1 Videos

  • Confidence Intervals

Assignment 5.1: Confidence Intervals and Capture Percentages

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Answer Form Assignment 5.1: Confidence Intervals and Capture Percentages

1 Readings

  • Assignment 5.1: Confidence Intervals

Lecture 5.2: Sample Size Justification

    • 1 Videos
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1 Videos

  • Sample Size Justification

Assignment 5.2: Random Variation and Power Analysis

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Answer Form Assignment 5.2: Random Variation and Power Analysis

1 Readings

  • Assignment 5.2: Random Variation and Power Analysis

Lecture 5.3: P-Curve Analysis

    • 1 Videos
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1 Videos

  • P-Curve Analysis

Exam Week 5

    • 2 Assignment
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2 Assignment

  • Pop Quiz 5!
  • Exam Week 5

Week 6: Overview

    • 1 Readings
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1 Readings

  • Week 6: Overview

Lecture 6.1: Philosophy of Science

    • 1 Videos
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1 Videos

  • Philosophy of Science

Lecture 6.2: The Null is Always False

    • 1 Videos
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1 Videos

  • The Null is Always False

Assignment 6: Equivalence Testing

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Answer Form Assignment 6: Equivalence Testing

1 Readings

  • Assignment 6: Equivalence Testing

Lecture 6.3: Theory Construction

    • 1 Videos
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1 Videos

  • Theory Construction

Exam Week 6

    • 1 Assignment
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1 Assignment

  • Exam Week 6

Week 7: Overview

    • 1 Readings
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1 Readings

  • Week 7: Overview

Lecture 7.1: Replications

    • 1 Videos
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1 Videos

  • Replications

Lecture 7.2: Publication Bias

    • 1 Videos
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1 Videos

  • Publication Bias

Lecture 7.3 Open Science

    • 1 Videos
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1 Videos

  • Open Science

Assignment 7: Open Science

    • 1 PeerReview
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1 Peer Review

  • Assignment 7: Open Science

Practice Final Exam

    • 2 Assignment
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2 Assignment

  • Pop Quiz 6!
  • Practice Exam

Graded Final Exam

    • 1 Assignment
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1 Assignment

  • Graded Final Exam

Auto Summary

"Improving your statistical inferences" is a comprehensive course in Data Science & AI, designed to enhance your ability to draw accurate statistical conclusions from empirical research. Led by an expert instructor, this course covers critical topics such as interpreting p-values, designing experiments to control false positive rates, and understanding publication bias. With hands-on assignments, you will practice simulations, Bayesian statistics, and power analyses. The course spans 1680 minutes and is available through various subscription options, catering to professionals seeking to deepen their statistical knowledge. Join over 30,000 learners and advance your expertise in statistical inference today!

Daniel Lakens
Instructor

Daniel Lakens