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
Universidad Técnica de EindhovenAcerca de este Curso
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Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
A basic knowledge of statistics and research methods is necessary. My previous MOOC 'Improving Your Statistical Inferences' is recommended.
Aprox. 15 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Qué aprenderás
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
Habilidades que obtendrás
Computational ReproducibilityMeta-AnalysisExperimental DesignStatistical InferencesPhilosophy of Science
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
A basic knowledge of statistics and research methods is necessary. My previous MOOC 'Improving Your Statistical Inferences' is recommended.
Aprox. 15 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
Universidad Técnica de Eindhoven
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.
Programa - Qué aprenderás en este curso
2 horas para completar
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 horas para completar
3 videos (Total 40 minutos), 2 lecturas, 3 cuestionarios
3 videos
Lecture 1.2: Do You Really Want to Test a Hypothesis?15m
Lecture 1.3: Risky Predictions12m
2 lecturas
Download Course Materials and Course Structure (Must Read)10m
Assignment 1.1: Testing Range Predictions30m
3 ejercicios de práctica
Consent Form for Use of Data10m
Welcome: Short Survey5m
Answer Form Assignment 1.1: Testing Range Predictions2m
3 horas para completar
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.
3 horas para completar
3 videos (Total 46 minutos), 3 lecturas, 3 cuestionarios
3 videos
Lecture 2.2: Setting the Smallest Effect Size Of Interest14m
Lecture 2.3: Falsifying Predictions in Practice15m
3 lecturas
Assignment 2.1: The Small Telescopes Approach to Setting a SESOI30m
Assignment 2.2: Setting the SESOI Based on Resources30m
Assignment 2.3: Equivalence Testing30m
3 ejercicios de práctica
Answer Form Assignment 2.1: The Small Telescopes Approach to Setting a SESOI8m
Answer Form Assignment 2.2: Setting the SESOI Based on Resources10m
Answer Form Assignment 2.3: Equivalence Testing18m
3 horas para completar
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 horas para completar
3 videos (Total 48 minutos), 2 lecturas, 2 cuestionarios
3 videos
Lecture 3.2: Power Analysis12m
Lecture 3.3: Simulation15m
2 lecturas
Assignment 3.1: Confidence Intervals for Standard Deviations30m
Assignment 3.2: Power Analysis for ANOVA Designs1h
2 ejercicios de práctica
Answer Form Assignment 3.1: Confidence Intervals for Standard Deviations12m
Answer Form Assignment 3.2: Power Analysis for ANOVA Designs20m
3 horas para completar
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.
3 horas para completar
3 videos (Total 48 minutos), 4 lecturas, 3 cuestionarios
3 videos
Lecture 4.2: Intro to Meta-Analysis17m
Lecture 4.3: Bias Detection15m
4 lecturas
Assignment 4.1: Likelihood of Significant Findings30m
Assignment 4.2: Introduction to Meta-Analysis30m
Assignment 4.3: Detecting Publication Bias45m
Assignment 4.4: Checking Your Stats10m
3 ejercicios de práctica
Answer Form Assignment 4.1: Likelihood of Significant Findings14m
Answer Form Assignment 4.2: Introduction to Meta-Analysis4m
Answer Form Assignment 4.3: Detecting Publication Bias14m
Revisiones
Principales revisiones sobre IMPROVING YOUR STATISTICAL QUESTIONS
por HS3 de dic. de 2019
I recommend this course to everyone who wants to improve their grasp of statistics. The course involves content that is timely and relevant within an easy-to-digest form and amount.
por MM17 de feb. de 2020
Dr. Lakens passes ET test with very tiny SESOI. As usual. I was suitable only for two tailed NHST without any direction of effect. Put another way: "anything goes" :o)
por AH11 de may. de 2020
Fantastic state-of-the-art and practical knowledge. It is very useful for researchers at any stage of the scientific career. Thank you, Daniel.
por SS2 de ene. de 2020
Excellent! Would like only one addition, and that's a more extensive exercise on simulating data with general linear models
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
- es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
- Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si compro el Certificado?
Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.
¿Cuál es la política de reembolsos?
Puedes solicitar un reembolso completo hasta dos semanas después de tu fecha de pago o (para los cursos que se lanzaron recientemente) hasta dos semanas después del comienzo de la primera sesión del curso, lo que ocurra después. No puedes recibir un reembolso luego de obtener un Certificado de curso, aun cuando completes el curso dentro del período de reembolso de dos semanas. Consulta nuestra política de reembolsos completa.
¿Hay ayuda económica disponible?
Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el vínculo de Ayuda económica que está debajo del botón 'Inscribirse' a la izquierda. Se te pedirá que completes una solicitud y recibirás una notificación cuando se apruebe. Obtén más información.
Which previous knowledge is required?
The course assumes basic knowledge about statistical inferences (t-tests, ANOVA) and some knowledge of designing research studies. The course is for intermediate level. Coursera offers basic introductions to statistics (which this course is not), and my previous MOOC 'Improving Your Statistical Inferences' might be a better starting point if you lack training in statistics. You do not need knowledge programming in R - we will use it as a fancy calculator by changing code (but not programming).
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