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Improving your statistical inferences

Daniel Lakens

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.

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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"

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What's inside

Syllabus

Introduction + Frequentist Statistics
Likelihoods & Bayesian Statistics
Multiple Comparisons, Statistical Power, Pre-Registration
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Effect Sizes
Confidence Intervals, Sample Size Justification, P-Curve analysis
Philosophy of Science & Theory
Open Science
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.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers a wide range of statistical concepts, including p-values, effect sizes, confidence intervals, and likelihood ratios
Taught by Daniel Lakens, a renowned expert in statistical inference
Provides hands-on assignments and simulations to reinforce learning
Emphasizes practical applications of statistical methods in research
Covers advanced topics such as sequential analyses, Open Science principles, and Bayesian statistics
May require a strong foundation in statistics for some learners

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Reviews summary

Engaging statistics course with critical analysis

Learners say that Improving your statistical inferences is an engaging course that helps students critically evaluate statistical inferences. Instructor Daniel Lakens presents concepts in clear and interesting ways with a focus on helping learners become critical consumers of statistical information. Through hands-on assignments using R programming and open science, learners develop a practical understanding of statistical methods and interpretation. This course is highly recommended for anyone interested in statistics, research, or data analysis.
Appropriate for learners with varying backgrounds in statistics
"I am so glad that i enrolled and completed this course. It is an excellently designed course and offer us an understanding on how critical decisions on inferential statistics are, which is definitely not taught in universities."
"This eight week course is well presented, with plenty of assessment opportunities to make sure you understand the topics as you go along."
Features well-explained lectures with thoughtful examples
"Excellent explanations. Strong examples. Helpful exercises."
Introduces concepts of open science and the importance of replication
"Excellent course. The rigor that has gone into the video's and downloadable materials is remarkable."
"A great course that goes beyond the mathematics. Mr. Lakens explains statistical inferences in plain English. The assignments help in establishing a critical thinking about p-values and statistical inferences printed in published science."
Teaches learners to critically analyze statistical results and interpretations
"Solid course which taught me how to interpret p-values in a variety of contexts and taught me to not just to consider but (systematic and practical) ways of how to correct for publication bias."
"This course is great! I learned a lot about statistics and how to have a critical thinking about tests and results in the literature. I also gain in confidence for doing statistics."
Includes practical assignments that give learners real-world experience in statistical analysis
"Very good course, with a lot of practical work, which is nice. Also very clear lectures explaining the topics and not too difficult but definitely not too easy exams! Overall fantastic course, which provided me interesting new insights."
"The best course I've ever taken on Coursera (and I've taken a dozen or more). Professor Lakens teaches with great care and attention and all the lessons are meticulously crafted, full of references and extra materials."

Activities

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Career center

Learners who complete Improving your statistical inferences will develop knowledge and skills that may be useful to these careers:
Biostatistician
Improving Your Statistical Inferences is a course that will help you strengthen the foundation you need to excel in the role of a Biostatistician. This course will build foundational skills in statistical analysis as well as teach the latest techniques and methods used in the field. This course will help you develop the statistical methods to assess the health effects of new drugs and medical devices and to improve the quality of healthcare delivery and is thus strongly recommended for those wishing to work as a Biostatistician.
Data Scientist
For someone working as a Data Scientist, enrolling in the course titled Improving Your Statistical Inferences is strongly recommended. This course will expand upon knowledge of statistical techniques, teach new methods of analysis, and will teach how to apply these methods to real-world problems. Furthermore, it will help you enhance your abilities to work with large and complex data sets, and to communicate findings clearly and effectively. With the knowledge you gain from this course, you will be well-equipped to contribute to the field of Data Science.
Quantitative Analyst
The course titled Improving Your Statistical Inferences can help equip you with the skills necessary to excel in the role of a Quantitative Analyst. This course will enhance your understanding of statistical methods and techniques, as well as teach you how to apply them to real-world problems. The knowledge gained from this course will help you in areas such as in making better investment decisions, managing risk, and developing new financial products.
Statistician
If your goal is to work as a Statistician, taking the course titled Improving Your Statistical Inferences is strongly recommended. This course will help you develop the skills necessary to design and conduct statistical studies, analyze data, and interpret results. You will also learn about the latest statistical methods and techniques, which will help you stay up-to-date on the latest advancements in the field. All of these will make you an even more valuable asset in your role as a Statistician.
Market Researcher
The course titled Improving Your Statistical Inferences may be helpful for those working as a Market Researcher. This course will provide you with the skills and knowledge necessary to design and conduct market research studies, analyze data, and interpret results. You will also learn about the latest statistical methods and techniques, which will help you stay up-to-date on the latest advancements in the field.
Financial Analyst
Improving Your Statistical Inferences is a course that may be useful for those working as a Financial Analyst. This course will provide you with the skills and knowledge necessary to analyze financial data, and interpret results. You will also learn about the latest statistical methods and techniques, which will help you stay up-to-date on the latest advancements in the field. Financial Analysts who are skilled in statistical analysis are highly valued in the field.
Economist
Improving Your Statistical Inferences is a course that may be useful for those working as an Economist. This course will provide you with the skills and knowledge necessary to analyze economic data, and interpret results. You will also learn about the latest statistical methods and techniques, which will help you stay up-to-date on the latest advancements in the field. Being well-versed in these statistical methods is highly valuable for an Economist.
Business Analyst
Improving Your Statistical Inferences could be helpful for someone working as a Business Analyst. This course will provide you with the skills and knowledge necessary to analyze business data, and interpret results. You will also learn about the latest statistical methods and techniques, which will help you stay up-to-date on the latest advancements in the field. Being well-versed in statistical analysis is an asset to have as a Business Analyst.
Actuary
Improving Your Statistical Inferences could be helpful for someone working as an Actuary. This course will provide you with the skills and knowledge necessary to analyze insurance and financial data, and interpret results. You will also learn about the latest statistical methods and techniques, which will help you stay up-to-date on the latest advancements in the field. Being well-versed in statistical analysis is an asset to have as an Actuary.
Data Analyst
Improving Your Statistical Inferences is a course that may be helpful for someone working as a Data Analyst. This course will provide you with the skills and knowledge necessary to analyze data, and interpret results. You will also learn about the latest statistical methods and techniques, which will help you stay up-to-date on the latest advancements in the field.
Software Engineer
The course titled Improving Your Statistical Inferences may be helpful for those working as a Software Engineer. The skills learned in this course will complement and add value to your current skill set. You will learn a variety of statistical methods and techniques, which could open up new opportunities in your current role or make you more competitive within the job market.
Teacher
The course on Improving Your Statistical Inferences could be helpful to you as a Teacher. Specifically, if you teach mathematics, science, or related subjects. This course will provide you with a deeper understanding of statistical methods and techniques, which you can use to enhance your own instructional practice. You will also be able to pass your new knowledge on to your students, giving them a leg up in a highly statistical world!
Academic Researcher
If you're looking to work as an Academic Researcher, the course titled Improving Your Statistical Inferences could be beneficial to your career. The course will provide you with the skills and knowledge necessary to design and conduct research studies, analyze data, and interpret results. You will also learn about the latest statistical methods and techniques, which will help you stay up-to-date on the latest advancements in research methodology.
Consultant
Improving Your Statistical Inferences is a course that may be helpful for those working as a Consultant. This course will provide you with the skills and knowledge necessary to analyze data, and interpret results. You will also learn about the latest statistical methods and techniques, which will help you stay up-to-date on the latest advancements in the field. By adding this to your skill set, you will become even more valuable as a Consultant.
Journalist
If you're a Journalist, taking the course titled Improving Your Statistical Inferences could add value to your work. This course will provide you with the skills and knowledge necessary to analyze data, and interpret results. You will also learn about the latest statistical methods and techniques, which will help you stay up-to-date on the latest advancements in the field. These skills will help you to better understand and report on complex statistical information.

Reading list

We've selected 25 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Improving your statistical inferences.
Provides a practical introduction to Bayesian statistics using R and Stan. It valuable resource for students and researchers interested in learning how to apply Bayesian methods to real-world problems.
Is intended to help you to think and reason about statistical problems. The book covers a wide range of topics, from basic concepts to advanced methods. It is written in a clear and engaging style, and it includes many examples and exercises to help you learn the material.
Provides a comprehensive overview of Bayesian statistics, including frequentist and Bayesian approaches to statistical inference. It valuable resource for students and researchers interested in understanding and applying Bayesian methods.
This foundational text provides an introduction to causal inference, covering graphical models, counterfactuals, and structural equation modeling, which would complement the course's discussions on statistical inference and the philosophy of science.
Provides a step-by-step guide to Bayesian data analysis using R, JAGS, and Stan. It valuable resource for students and researchers interested in learning how to use Bayesian methods in practice.
Provides a comprehensive overview of statistical learning methods, including supervised and unsupervised learning, regression, and classification. It valuable resource for students and researchers interested in learning about the latest advances in statistical learning.
This practical guide to meta-analysis using the R software would enhance the course's coverage of statistical power and effect sizes, providing participants with hands-on experience in synthesizing research findings.
Provides a comprehensive overview of statistical methods used in psychology, including descriptive statistics, inferential statistics, and multivariate analysis. It valuable resource for students and researchers interested in learning about the application of statistical methods to psychological research.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, from basic concepts to advanced methods. It is written in a clear and concise style, and it includes many examples and exercises to help you learn the material.
Provides a comprehensive introduction to statistical methods for social and behavioral sciences. It covers a wide range of topics, from basic concepts to advanced methods. It is written in a clear and concise style, and it includes many examples and exercises to help you learn the material.
Provides a comprehensive introduction to statistical methods for the social sciences. It covers a wide range of topics, from basic concepts to advanced methods. It is written in a clear and concise style, and it includes many examples and exercises to help you learn the material.
Provides a comprehensive introduction to statistical methods for the behavioral sciences. It covers a wide range of topics, from basic concepts to advanced methods. It is written in a clear and concise style, and it includes many examples and exercises to help you learn the material.
Provides a comprehensive introduction to statistics and data analysis. It covers a wide range of topics, from basic concepts to advanced methods. It is written in a clear and concise style, and it includes many examples and exercises to help you learn the material.
Provides a comprehensive overview of statistics, including probability, inference, and regression. It valuable resource for students and researchers interested in learning the fundamentals of statistics.
Provides a comprehensive introduction to statistical methods for research. It covers a wide range of topics, from basic concepts to advanced methods. It is written in a clear and concise style, and it includes many examples and exercises to help you learn the material.
Provides a gentle introduction to statistical learning methods, including supervised and unsupervised learning, regression, and classification. It valuable resource for students and researchers interested in getting started with statistical learning.
Provides a comprehensive introduction to generalized linear models. It covers a wide range of topics, from basic concepts to advanced methods. It is written in a clear and concise style, and it includes many examples and exercises to help you learn the material.
Provides an introduction to Bayesian statistics. It covers a wide range of topics, from basic concepts to advanced methods. It is written in a clear and concise style, and it includes many examples and exercises to help you learn the material.
Provides a comprehensive introduction to statistical inference. It covers a wide range of topics, from basic concepts to advanced methods. It is written in a clear and concise style, and it includes many examples and exercises to help you learn the material.
Provides a comprehensive introduction to statistical methods for psychology. It covers a wide range of topics, from basic concepts to advanced methods. It is written in a clear and concise style, and it includes many examples and exercises to help you learn the material.
Provides a comprehensive overview of multivariate statistics, including factor analysis, discriminant analysis, and cluster analysis. It valuable resource for students and researchers interested in learning about the application of multivariate methods to real-world problems.
Provides a non-technical introduction to statistics, including descriptive statistics, inferential statistics, and regression. It valuable resource for students and researchers interested in learning about the basics of statistics without getting bogged down in the details.
Covers Bayesian inference, decision theory, asymptotic theory, the bootstrap, and other cutting-edge statistical topics, which could enhance the course's discussions on statistical theory and the philosophy of science. However, this book is more suited as a textbook for formal academic study than as a supplemental reading resource.

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