This course aims to help you to ask better statistical questions when performing empirical research. We will discuss how to design informative studies, both when your predictions are correct, as when your predictions are wrong. We will question norms, and reflect on how we can improve research practices to ask more interesting questions. In practical hands on assignments you will learn techniques and tools that can be immediately implemented in your own research, such as thinking about the smallest effect size you are interested in, justifying your sample size, evaluate findings in the literature while keeping publication bias into account, performing a meta-analysis, and making your analyses computationally reproducible.
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Improving Your Statistical Questions
Технический университет ЭйндховенаОб этом курсе
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Начните сейчас и учитесь по собственному графику.
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Средний уровень
A basic knowledge of statistics and research methods is necessary. My previous MOOC 'Improving Your Statistical Inferences' is recommended.
Прибл. 18 часов на выполнение
Английский
Субтитры: Английский
Чему вы научитесь
Ask better questions in empirical research
Design more informative studies
Evaluate the scientific literature taking bias into account
Reflect on current norms, and how you can improve your research practices
Приобретаемые навыки
Computational ReproducibilityMeta-AnalysisExperimental DesignStatistical InferencesPhilosophy of Science
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Средний уровень
A basic knowledge of statistics and research methods is necessary. My previous MOOC 'Improving Your Statistical Inferences' is recommended.
Прибл. 18 часов на выполнение
Английский
Субтитры: Английский
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Технический университет Эйндховена
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.
Программа курса: что вы изучите
2 ч. на завершение
Module 1: Improving Your Statistical Questions
One of the biggest improvements most researchers can make is to more clearly specify their statistical questions. When you perform a study, what is it you really want to know?
2 ч. на завершение
3 видео ((всего 40 мин.)), 2 материалов для самостоятельного изучения, 3 тестов
4 ч. на завершение
Module 2: Falsifying Predictions
There is little use in making predictions if you can never be wrong - so how do we make sure your predictions are falsifiable? We discuss why falsifiable predictions are important, and how to make your predictions falsifiable in practice. One important aspect of making predictions falsifiable is to specify a range of values that is not predicted, and we will examine different approaches to specifying a smallest effect size of interest.
4 ч. на завершение
3 видео ((всего 46 мин.)), 3 материалов для самостоятельного изучения, 3 тестов
3 ч. на завершение
Module 3: Designing Informative Studies
If studies are designed to answer a question, you should make sure the answer you will get after collecting data is informative. Instead of mindlessly setting Type 1 and Type 2 error rates, we will learn why it is important to be able to justify error rates, and some approaches how to do so. We discuss the benefits of using your smallest effect size of interest in power analyses, and why learning to simulate data is a useful tool. Simulations can help you to improve your understanding of statistics, enable you to design informative studies, and even ask novel questions.
3 ч. на завершение
3 видео ((всего 48 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
4 ч. на завершение
Module 4: Meta-Analysis and Bias Detection
Regrettably we work in a scientific enterprise where the published literature does not reflect real research. Publication bias and selection biases lead to a scientific literature that can’t be interpreted without taking these biases into account. We will discuss what real research lines look like, and how to meta-analytically evaluate the literature while keeping bias in mind.
4 ч. на завершение
3 видео ((всего 48 мин.)), 4 материалов для самостоятельного изучения, 3 тестов
Рецензии
Лучшие отзывы о курсе IMPROVING YOUR STATISTICAL QUESTIONS
от партнера AH11 мая 2020 г.
Fantastic state-of-the-art and practical knowledge. It is very useful for researchers at any stage of the scientific career. Thank you, Daniel.
от партнера SS2 янв. 2020 г.
Excellent! Would like only one addition, and that's a more extensive exercise on simulating data with general linear models
от партнера SW31 дек. 2019 г.
Cracking - very informative, nice mixture of modes of learning, and engaging
от партнера LP30 окт. 2019 г.
Daniel's second course as good as the first. He does a nice job!!
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