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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- 5 stars88.48%
- 4 stars9.90%
- 3 stars1.07%
- 2 stars0.26%
- 1 star0.26%
IMPROVING YOUR STATISTICAL INFERENCES의 최상위 리뷰
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.
Great course to dig a bit deeper into some very useful statistical concept. 4 starts as many of the contents are not "open" as the course preaches (see Microsoft Office documents or GPower).
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).
Sooo good! Cant even begin to explain how essential and wonderful this understanding is!
Great thanks to Dr Daniel! Such an expert in the field!
Thank you Dr!
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