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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Improving your statistical inferences
埃因霍温科技大学课程信息
您将获得的技能
- Likelihood Function
- Bayesian Statistics
- P-Value
- Statistical Inference
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埃因霍温科技大学
Eindhoven University of Technology (TU/e) is a young university, founded in 1956 by industry, local government and academia. Today, their spirit of collaboration is still at the heart of the university community. We foster an open culture where everyone feels free to exchange ideas and take initiatives.
授课大纲 - 您将从这门课程中学到什么
Introduction + Frequentist Statistics
Likelihoods & Bayesian Statistics
Multiple Comparisons, Statistical Power, Pre-Registration
Effect Sizes
审阅
- 5 stars88.55%
- 4 stars9.94%
- 3 stars0.95%
- 2 stars0.27%
- 1 star0.27%
来自IMPROVING YOUR STATISTICAL INFERENCES的热门评论
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.
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.
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.
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).
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