Voltar para Improving your statistical inferences

4.9

estrelas

575 classificações

•

180 avaliações

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"...

Jun 29, 2020

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.

Mar 02, 2017

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.

Filtrar por:

por Martine K

•Jun 21, 2018

Really great course! Was already familiar in statistics, but learned a lot about making inferences based on statistical tests. Lectures and assignments are very clear. Would recommend it to everyone interested in statistics.

por Esthelle E

•Jan 23, 2019

It was truly an awesome course! I learned a lot from the very well done videos, and well thought-through assignment. Would recommend to anyone trying to marry theory and application in ways that are actually helpful! BRAVO!

por Stephen S

•Jun 10, 2020

Such a great course. Daniel Lakens does a fantastic job explaining the nuances of statistical repeatability with well thought out examples and helpful tools. This is hands down of the best Coursera courses I've completed.

por Max K

•Nov 28, 2019

This course will actually improve your statistical inferences. It's helpful to get an overview and better understanding of different statistical approaches and a nice introduction into Baysian stats. Would do it again!

por Meghana J

•Oct 17, 2019

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!)

por Jaroslav G

•Feb 05, 2018

I found this course very well-structured and easily accessible and understandable even to students, while being highly profound and covering most important and and recent pressing topics in methodology and statistics.

por Srinivas K R

•Oct 09, 2017

A course taught by a single individual - that packs more learning and knowledge into it than many rote courses. A course that I have returned to and will return to many times in the future to brush up on fundamentals.

por Jonas S

•Nov 16, 2016

Very well designed course, from a didactic as well as from an entertainment point of view. I was able to close many gaps in my inferential statistics knowledge and now feel much more confident in my interpretations.

por Rebecca W

•Jul 17, 2017

An accessible and interesting course. I learned so much (and refreshed myself on things I should already know!). Thank you so much Dr Lakens for putting together this course. I've been recommending it to everyone!

por Carlos L F

•Jul 18, 2017

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.

por Aishwar D

•Aug 25, 2018

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

por Paul

•Jun 29, 2020

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.

por Alvaro M B

•Feb 24, 2020

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.

por Yaron K

•Mar 02, 2017

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.

por Andrés C M

•Mar 25, 2019

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.

por Miroslav R

•Feb 22, 2018

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

por Bob H

•Oct 06, 2017

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).

por Tiago C Z

•Jun 19, 2018

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.

por Rizqy A Z

•Jul 10, 2018

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

por Yashar Z

•Oct 17, 2016

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)!

por Marcin K

•Dec 22, 2016

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.

por Kevin H

•May 13, 2019

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.

por Jakob W

•Jan 05, 2018

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!

por Hendrik B

•Nov 18, 2017

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.

por Shunan H

•Oct 15, 2019

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.

- IA para todos
- Introdução ao TensorFlow
- Redes neurais e aprendizagem profunda
- Algoritmos, parte 1
- Algoritmos, parte 2
- Aprendizagem Automática
- Aprendizagem automática com Python
- Aprendizagem automática usando o Sas Viya
- Linguagem R
- Introdução à programação com Matlab
- Análise de dados com Python
- Fundamentos da AWS: Going Cloud Native
- Fundamentos da Google Cloud Platform
- Engenharia de confiabilidade do site
- Fale inglês profissionalmente
- A ciência do bem-estar
- Aprendendo a Aprender
- Mercados Financeiros
- Testes de hipóteses em saúde pública
- Princípios da liderança no cotidiano

- Aprendizagem profunda
- Python para todosPython para todos
- Ciência de Dados
- Ciência de dados aplicada com Python
- Fundamentos de negóciosFundamentos dos Negócios
- Arquitetura com o Google Cloud Platform
- Engenharia de dados em Google Cloud Platform
- Excel para MySQL
- Aprendizagem de máquina avançada
- Matemática para aprendizagem automática
- Carros autoguiáveis
- Revolução do Blockchain para a empresa
- Análises empresariaisAnálises Empresariais
- Habilidades em Excel para negócios
- Marketing digitalMarketing Digital
- Análise estatística com R para saúde pública
- Fundamentos da imunologia
- Anatomia
- Gestão da inovação e Design Thinking
- Princípios da psicologia positiva