Improving your statistical inferences(으)로 돌아가기

4.9
별점
715개의 평가
236개의 리뷰

## 강좌 소개

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"...

## 최상위 리뷰

MS

2021년 5월 13일

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.

YK

2017년 3월 1일

Excellent course. The lecturer has written code snippets that let the students visualize the meaning and interrelationship of p-values confidence-intervals power effect-size bayesian-inference.

필터링 기준:

## Improving your statistical inferences의 235개 리뷰 중 76~100

교육 기관: Carlos L F

2017년 7월 18일

It's a really interesing course about statistical inferences. You can learn a lot about how to recollect data, how to analyse it and how to interpret it. It is very recommendable for all kind of researchers.

교육 기관: Mark G

2021년 5월 9일

This is an excellent course, one which I’d recommend to anybody with an interest in science, or open science, whether you be a scientist or just someone with an interest. Daniel does an excellent job here.

교육 기관: Mark S

2021년 5월 14일

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.

교육 기관: Aishwar D

2018년 8월 25일

Thank you Daniel Lakens for creating and sharing this course in the way you have done. The content is very appropriate for any one anyone who is looking to work with Inferential Statistics. Many thanks

교육 기관: Paul

2020년 6월 29일

Excellent explanations. Strong examples. Helpful exercises. Highly recommended for anyone who ever has to conduct inferential statistics or read anything that reports a p value or bayes factor.

교육 기관: Alvaro M B

2020년 2월 23일

Easy to follow, well structured, good references, empathy of presenter. I will recomend this to other friends who made Black Belt certification and still don't have clear what the Pvalue is for.

교육 기관: Victor M

2021년 7월 11일

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.

교육 기관: Yaron K

2017년 3월 2일

Excellent course. The lecturer has written code snippets that let the students visualize the meaning and interrelationship of p-values confidence-intervals power effect-size bayesian-inference.

교육 기관: Georgia P

2021년 6월 18일

Really enjoyed this course! The content was perfect to get my stats brain raring to go for my PhD, and now I can go in with a much better insight on interpreting my findings from the get go.

교육 기관: Andrés C M

2019년 3월 25일

Excellent course. I improved my statistical knowledge and learned more about bayesian inference. Also, I learned something about how to pre-register a research and its benefits of doing so.

교육 기관: Alvaro B

2022년 3월 23일

Very complete an instructional. This is a very compled topic and concepts stick in your mind through the explanations and materials prepared by Lakens and the exams throghout the course.

교육 기관: Miroslav R

2018년 2월 22일

Excellent course with a lot to learn. After 10 years in data analysis it provided me with great new insights and material to further improve my skills and understanding of data analysis

교육 기관: Bob H

2017년 10월 6일

This is a top-notch course. The ground (especially pitfalls) is very well covered, and useful free tools are engaged (R, G*Power, prof's own spreadsheets for calculating effect size).

교육 기관: Turgon R

2020년 11월 27일

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.

교육 기관: Tiago Z

2018년 6월 19일

This course changed my concepts not only about statistics but about research and science. Daniel Lakens is a fantastic lecturer and scientist. I can't recommend this course enough.

교육 기관: Rizqy A Z

2018년 7월 10일

This course is immensely helpful to improve my area of expertise. This course also fills the gap of my previous formal training with current challenges in my career as a scientist

교육 기관: Yashar Z

2016년 10월 16일

Really nice course! begins from basics but gives you a deeper understanding of concepts. Plus the quizzes are open for auditing (as one expects from an open science advocate)!

교육 기관: Marcin K

2016년 12월 22일

Great course. Daniel explains everything clearly and with examples in R code which makes all of the concepts easier to understand. A must-take for experimental psychologists.

교육 기관: Kevin H

2019년 5월 13일

Very good introduction course. An improvement could be to include more high level summaries of each sections. I think it could help students better organize their thoughts.

교육 기관: Jakob W

2018년 1월 5일

Hi! Thanks a ton for a spectacular course. I pick up new understanding every week here, and I actually look forward to going through the material each week. So great job!

교육 기관: Hendrik B

2017년 11월 18일

One of the best courses I have done so far on Coursera. Fairly advanced and very helpful for (under-) grad students running experiments or working with data in general.

교육 기관: Shunan H

2019년 10월 14일

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.

교육 기관: Gregory D

2018년 3월 26일

Excellent course. Must take for any students interested in doing scientific research, especially in the domain of the social sciences. Very interesting and informative.

교육 기관: Glenn

2017년 6월 21일

Excellent course. The materials were well laid out and explained in an accessible but thorough manner. I've already begun using what I've learned in my current work.

교육 기관: Andres F P A

2021년 8월 16일

Really good course! The course reviews several common statistical methods and tools used in research and strive to help the student on their interpretation.