Вернуться к Improving your statistical inferences

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Оценки: 703

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Рецензии: 234

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"...

MS

13 мая 2021 г.

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.

VM

10 июля 2021 г.

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.

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автор: Daniel A L

•25 мая 2019 г.

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!

автор: Shan G

•25 июня 2018 г.

This courses uses R

автор: Bartek

•30 окт. 2016 г.

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.

автор: Stefan W

•28 дек. 2016 г.

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.

автор: Luis A

•21 авг. 2017 г.

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.

автор: Alex G

•26 окт. 2016 г.

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.

автор: Julien B

•21 июля 2019 г.

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.

автор: Pepe V C

•1 июня 2019 г.

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

автор: Yonathan M P

•8 июня 2019 г.

Amazing course! Tons of insights and original thinking!

автор: Farhan N

•21 мая 2018 г.

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!

автор: Benedikt L

•22 июня 2018 г.

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!

автор: Andreas K

•15 июля 2019 г.

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.

автор: Constantin Y P

•17 мая 2017 г.

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.

автор: Nicholas J

•23 янв. 2018 г.

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!

автор: Maxine S

•3 янв. 2022 г.

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.

автор: Oviya M

•18 июля 2020 г.

This course was much more than I expected it to be when I started it. I had always been used to a science of exact measurement till recently and have had my inhibition to using statistical inferences of these kinds. Though my concerns have not been completely alleviated, knowing that my concerns are not only shared but are also actively under the process of being rectified was reassuring!

As far as the course structure is concerned, the overall format, the videos and especially the assignments were quite amazing! It was definitely captivating and at the end, it felt like a very satisfying journey. I hope there are more courses from Dr. Lakens in the future.

автор: Răzvan J

•30 мая 2017 г.

This was a very usefull learning experience. It helped me to understand better at a conceptual level many statistical methods that are not taught very throughtly in formal education (e.g., Bayesian inference, equivalence testing etc). However, the biggest gains come from the many practical exercises at the end of each module. As a suggestion, in Week 8 I think there should be an additional recapitulation/practice quiz that should consist in more practical exercises (e.g., calculating likelihoods or posterior probabilities, effect sizes etc). Now week 8 (the practice quiz and the final exam) tests the content almost exclusively at a conceptual level.

автор: Jason L

•7 дек. 2018 г.

I really enjoyed the course and found it challenging at times. Its definitely worth the time and effort as my knowledge has improved dramatically. I have gained knowledge which will be really helpful in the future for correctly interpreting current literature as well as future reporting of data and building research ideas. I also appreciate all the effort put into this course and the tools provided which will be beneficial to me in the future. I have saved a lot of the webpages and tools for future reference and will definitely use them when beginning research as well as examining current literature. Excellent

автор: Helén L

•17 авг. 2018 г.

The course was great for refreshing my understanding of statistical inferences. Additionally, it provides an easy to understand introduction to bayesian thinking. The apps and websites, as well as the R-codes and excel-sheets provided alongside the assignments, and the lecture videos are of high quality and proof of a thorough and intesive preparation of the material. The material is very helpful, both for learners and for those teaching statistics to students. Plus, Professor Lakens lectures are entertaining and fun to watch.

I really enjoyed the course and have already recommended it to my department.

автор: Tyson W B

•23 февр. 2018 г.

An excellent course! I've taught undergraduate statistics in psychology and consider myself reasonably well-versed in statistics and this was a very helpful expansion.

The course focuses on concepts rather than equations and R programming. Equations are presented, but the focus is on the concept underlying the equation. This course uses R as the analysis software and I had no prior experience with R, but that was not a problem as the instructions are detailed enough to follow along while focusing attention on the statistical concepts.

автор: zuzana n

•18 сент. 2020 г.

This is without any doubt the best course in inferential statistics I have ever taken! I loved how comprehensive and organized the materials were and how during the course we had so many practical exercises related directly to the topics covered in the lecture videos. The final assignment has put into use everything covered in the course, which was yet another way to practise the things learned during the module. Moreover, the course has provided me with tons of useful R code that I can use later on for my own purposes.

автор: Oaní d S d C

•16 авг. 2018 г.

The course taught me a lot about data analysis and the philosophy of science. By focusing on the processes associated to doing science (data collection, theory generation, statistical inference) the course prepares you to design studies and think better about any area of research (it`s all data after all). But not just that, it made me rethink various things I do in life. I have to say that while, and now after, doing it I started to take a more scientific and data driven approach to all problems in my life. 10/10

автор: Yoel S

•15 сент. 2018 г.

One of the best online courses I've ever taken! (completed it just now). Great lectures, great materials, great assignments. Links and information for anyone wanting to go deeper on any topic. Brilliant and engaing lecturer who provides the information with so much passion and interest that it "catches on" to you. I especially liked how actual studies are used as examples for learning/assignments. Bottom line - in my opinion it's a must do course to anyone who is interested in inferential statistics.

автор: José M O

•6 дек. 2019 г.

An excellent course: full of training and insightful approaches. It peruses and smartly debunks the most ingrained rituals associated with statistical reasoning and practice (especially for a researcher psychologist), sometimes with a grain of subtle humor. Plenty of support literature and invaluable online tools (and others such as excel files) to understand and deal with each subject in both the assignments and hopefully, in future work. Very pleased to have taken the course!

автор: Jiyoung M

•12 янв. 2022 г.

I'm genuinely happy I took this course. I've learned statistical inference several times throughout my bachelor/master study but haven't had a lecturer or course that explained such an important aspects of what it actually means to do a correct and well-performed statistical inference. As a person who's preparing further study & career path in academia (psychology), this was such a valuable learning both theoretically and practically (for my own future researches).

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