This courses uses R

As an early career scientist, this course helped me get a solid foundation on statistical inferences. After years of accumulating vaguely-organised statistical concepts and procedures, now I am confident I have mastered the basics. Definitely the best course I've had in a long time!

Dr. Lakens is a very good instructor. He speaks cleary and he is extremaly focused in each subject he's teaching, Unfortunatelly, he keeps making some jargons in somehow he understand frequentist statistics. I'll list some of mistakes:

1. The p-value is a probability computed assuming *the null hypothesis is true*, that the test statistic would take a value as extreme or more extreme than that actually observed. When he cite "assuming null effect", he merge "effect size" and "NHSTs". This becomes even worst when we use NHST to analyze variable distributions where, by default, we don't have an "effect", but an "assumption". This is valid for all normality test, such anderson-darling or kolgomorov-smirnoff.

2. Furthermore considering the way he decided to approach to null hypothesis, any statistician knows that a null is always wrong and it is the why we dont accept the null. During all the time, in his videos, he insists to use "accepting the null". When he does that, is like a broken guitar in a symphony. It disturbs the video.

3. The control of type II error always involves some sample-size calculations wether we want to acchieve, at minimium, 80% of power. He simply attached a R script to run and he didnt't mention how we can verify if some study has an effect or not. Point and clicking button, in my opinion, is not adequate when we are in a statistical class where the goal is to improve our inferencial skills.

4. Some of quizzes and evaluations have items where options are not presented in a properly way. The subject of each response vary substantly.

I trully hope this feedback will be read in an academic way, which was the intention.

This course really delivers on its core premise: it helped me understand the core principles behind frequentist statistics, gave me some basic understanding of Bayesian statistics, and will definitely prevent me from chasing my tail as far as potential future research is concerned.

Although the course seems to be addressed to current and future researchers, I would recommend taking it to anyone interested in science as it will give you tools to read and understand research papers (esp. the basic reports in social science/experimental psychology).

I would consider this course an excellent resource and introduction to the so-called "new statistics", and covers topics crucial to conducting reproducible research.

The lectures are wonderfully taught and explain everything clearly. The hands-on assignments are challenging for the right reason: they test your knowledge and comprehension of the material at hand (some on-line courses did a number on me, and required extracurricular knowledge in order to succeed with completing the assignments).

I think that even stats newbies might be able to take the course and learn a lot, as most of the material pretty much addresses the basic, core philosophy of statistics, and you don't need to know how to conduct specific tests in order to understand what Daniel is trying to share with you.

This course is *the* course for anyone eager to understand what their stats 101 classes failed to even address.

This course is totally awesome! Statistical inference is critical in any science. Why collect data if we do not know what to infer from the data? Unfortunately, many disciplines use outdated or incorrect practices. This course provides an excellent review of state of the art approaches and provides students with many thought-proving opportunities to practice their inferential skills. As a professor of Psychology, I am not embarrassed to say that I learned lots from this course. The lectures, demos, and R scripts are useful tools that I will integrate in my teaching and my own research. Although the course topic is challenging, the course is organized well and does not drown students in technical terms. However, if you take this course, you better be serious and dedicated. The course is challenging, but the knowledge and skills gained are a rewarding experience.

To get this out of the way: The one star deduction is not related to the content of the course, only to the fact that there is occasional imprecise language and some parts of the material have typos and grammatical slip-ups that show that the course has room for some tightening up.

That being said, the selection of topics that are covered is great. You get a small but full package of both knowledge and tools that'll help you to significantly (no pun intended) improve your research. Not only are statistical pitfalls covered and solutions offered, you also learn something about how to approach your research with the right mind-set in order to produce solid empirical knowledge that contributes to a cumulative science.

I was particularly impressed by how the instructor manages to pack lots of important topics and concepts into his 10 or 15 minutes lectures without it becoming overwhelming. The key to this is his ability to maintain focus and his generally clear and concise language. The course material, too, reflects the ability to present just the right amount of information - not too little, not too much.

Overall, the course feels very pragmatic and hands-on. It proves that good and fruitful science is doable and that you can start right now. It makes you *want* to start right now.

Eye opening course. My first introduction to some of the issues surrounding p-values as well as how to better utilize them and what they truly represent. My first introduction to effect sizes as well.

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.

The explanations from Daniel are awesome... I am understanding p values in a manner I never did before.

Amazing course! Tons of insights and original thinking!

Good afternoon, I have finished my course since 5th November and I didn't get my certificate yet.

I found out about this course as i stumbled across Professor Daniel's blog one day and i feel very lucky that i did. Chances are, like me, you are making some very common mistakes in using and interpreting statistics which is why this course is a MUST for anyone in a discipline that uses statistics and i wholeheartedly recommend it to anyone who has taken a few introductory courses on the subject, regardless of their level of expertise.

The instructor goes through very real and practical topics in the use of statistics and weaves it with adequate theory, examples through simulations, exercises and plenty of additional sources. Common mistakes are highlighted and very useful solutions/tips are provided. The level of difficulty is very accessible and there is not much mathematics beyond algebra and basic probability, although you can go more in depth into technical supplementary readings, should you choose to do so. The instructor also replied to queries and helped out where he could. There is also a really good corresponding (although independent) facebook group on methodology that is very informative and from which i learn new things everyday.

This course is one of the main reasons i am now learning more mathematics so i can properly use statistics in my field of study (Psychology) and i would like to thank professor Daniel for making such a wonderful, eye opening resource for everyone who uses statistics.

Enroll as soon as you can!

This course was a great opportunity to reflect my statistical inference knowledge. I hold a master of science in psychology and already learned most of the stuff presented. But the course gave a great overview of the fundamentals of statistical inferences and made me really think twice about how to conduct science properly. I was able to deepen my knowledge and improved my understanding of the statistical fundamentals. I even learned a lot new things that were not covered in the university courses I had! The course is thus not only for beginners, but also for people who already have some knowledge in statistics. Also the course was really enjoyable and had just the right amount of information within each section. All the materials - videos, examples, further readings, exercises and pop-up-quizes varied and were very well designed! The examples were practically relevant (often based on real studies in the literature and not just artificially constructed) and sometimes also really humorous. Thanks a lot to the lecturer for this great opportunity to improve my knowledge!

It is a great course! I applied statistics for many years in my data analysis work. But during taking this course, I was shocked to see my understanding of p-value and other basic statistics concept was so poor. This course covers the very basic and fundamental knowledge of statistics, and I feel it is so useful and important in my work!

The teacher is very good and kind. I had a question and got very polite and accurate response in the same day! His lectures are very clear in speaking and presentation. All lecture materials are very well written and organized, slides, assignment, extra tools and links. Thank you so much!

I don't know how many hours other people used, but the time I spent on this course was doubled than the 28 hours as it indicates. Because I should read the material carefully and read extra reference paper, sometime need repeat the lecture. And the assignment of week7 took me much longer time than the indicated 3 hours. Well, I am proud that I have completed all exams in good.

I would definitively recommend it for someone who is interested into performing statistical studies and design experiments. The content of the course is very well summarized on this web page: https://lakens.github.io/statistical_inferences/ It is still an introductory level course, but to follow the class one needs to have the foundations of statistics. The course builds on top of this and is teaching us how to properly use the statistical tools to design better experiments without inflated results and report scientific research more transparently. The use cases are from the behavioural psychology, but this should not be a limiting factor. There are graded assignments structured as mini tutorials for which we need to play with the provided R scripts to answer the questions. For the ones that are more interested into the topic, there are plenty of references on the covered subjects.

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.

Great course for getting to know heterodox statistical paradigms, how open science could improve the scientific endeavor as a whole, the reasons that led to the replication crisis in some scientific areas and how to correct them. Due to this course I feel more confident analyzing scientific papers, meta-analysis and study designs. It also gave me great tools for conducting my own research, like getting to know the TIER protocol and the pre-registration process. This course awakened my interest in philosophy of science to a degree that I will start a second master´s degree in history and philosophy of science next semester. Prof. Lakens is excellent at making complex issues simple to understand, his videos are entertaining, informative and very well thought out.

A very informative course that has allowed my to better grasp ideas some concepts and introduced a few concepts which were new to my. The lectures were very well thought out. The quizzes assured I kept focus during lectures and reinforced the learning proces. The assignments were a good method for getting hands-on with the concepts introduced in the lectures. I highly appreciate that code examples have are supplied. This has allowd me to play what-if games with practical work-related topics, increasing both my understanding of how to use these concepts and tools in daily practice, as well as my understanding of some of the topics I have been working on recently. I will definitely be taking the follow-up cource.

One of the most valuable MOOC experiences I have ever encountered. Thank you, Dr. Lakens for creating such a worthwhile course! (Note that the course assignments are time-consuming, but they are well-designed and demonstrate concepts well.)

I have a PhD in Economics and wish this MOOC opportunity was available during my first year as a graduate student. It would have helped me immensely. Moreover, I wish that the leadership and members of the lab I used to work for would have also taken this course or at least not superficially accept the core principles of the open science culture that were demonstrated in this course. It would have minimized the bad research practices that were going on there!

This eight week course is well presented, with plenty of assessment opportunities to make sure you understand the topics as you go along. I felt that this course provided me with some foundational knowledge about making statistical inferences which I was lacking. Additional readings (mostly open access) are provided if you have time to delve deeper. Some exercises are in R, and require you to execute the scripts. I think some knowledge of R would be beneficial, but it would be possible to do the course without ever having used R. I would recommend this course to postgraduate students before they begin reading for their research proposal as the course helps one evaluate research better.