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Improving your statistical inferences

4.9
147 reviews

Created by: Daniel Lakens

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Course Description

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!
Read More product length iconTime: 22h

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Instructor Details

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Daniel Lakens is an Associate Professor in the Human-Technology interaction group at Eindhoven University of Technology (TU/e). His areas of expertise include meta-science, research methods and applied statistics. Daniel's main lines of empirical research focus on conceptual thought, similarity, and meaning. He also focuses on how to design and interpret studies, applied (meta)-statistics, and reward structures in science.-A large part of his work deals with developing methods for critically reviewing and optimally structuring studies.

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Reviews

4.9

147 reviews in total

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By Marko D on 8-Nov-19

Amazing course! Very useful to researchers in any area

By Suraj U M S on 5-Nov-19

This is a great course!. It tackles common misbeliefs and approaches the topics both in a technical and coloquial manner.

By Helen H I L on 17-Oct-19

The course is well-structured and excellently taught. The content is well researched and presented. The assignments are very practical and educative. (The philosophical references in the course content were on point!)

By Abinaya P S on 15-Oct-19

I like this course so much, Prof. Jeff makes all lectures clearly, but some answers and details in quizs are not mentioned in video and I have some problems with them.

By John P on 1-Oct-19

Excellent—an absolute must for all PhD students and early-career researchers

By Cesare M on 7-Sep-19

This course was fantastic. I believe I learned more in this class than I learned in three formal behavioral statistics courses. I highly recommend this course to other grad students, and I look forward to the next course that Lakens is creating!

By Yafei X on 14-Aug-19

Great course. Already had some knowledge about statistics, but this course really improved it.

By Ferran on 30-Jul-19

Excellent course, taught well with very useful assignments. Would recommend!

By Joseph T on 29-Jul-19

Very informative.

By Jeet D on 21-Jul-19

Amazing course! Many thanks to Daniel Lakens for the time spent on this. It's really useful and I've learned so many things I will use to make better research.

By Kishor K on 15-Jul-19

While the course is for researchers, also non-researchers like myself can get a better understanding for methods and pitfalls in science. You need to have prior knowledge of basic statistics and how to perform statistical tests, such as a t-test. I read up on the latter on the Internet, which proved sufficient.Most examples are from psychology, but the principles are general. In this brief course, very little mathematics is used, but there are other sources for that. The section on r class effect sizes could have used some more work. (Or perhaps I should know more beforehand?) The final exam may ask questions not explicitly covered in the material; I do not recall any mention of Bonferroni correction, but this is perhaps so basic that it is considered a prerequisite.

By Robson G D on 21-Jun-19

I started just today and I'm beginning to love the course

Showing 12 of 147 reviews

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Overall Score : 98 / 100

Improving your statistical inferences
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  • Video Duration: icon 22h
  • Price: Free
  • Provider: icon Coursera
  • University: icon Eindhoven University of Technology
  • Language: icon English
  • Certificate: icon Yes

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