Improving your statistical inferences

ALUMNOS MATRICULADOS
    ¡Compártelo! Share on Facebook0Share on LinkedIn0Share on Google+0Tweet about this on Twitter


    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.

    Syllabus

    Introduction + Frequentist Statistics

    Likelihoods & Bayesian Statistics

    Multiple Comparisons, Statistical Power, Pre-Registration

    Effect Sizes

    Confidence Intervals, Sample Size Justification, P-Curve analysis

    Philosophy of Science & Theory

    Open Science

    Final Exam
    This module contains a practice exam and a graded exam. Both quizzes cover content from the entire course. We recommend making these exams only after you went through all the other modules.

    Opiniones del Curso

    N.A.

    ratings
    • 5 stars0
    • 4 stars0
    • 3 stars0
    • 2 stars0
    • 1 stars0

    No se han encontrado opiniones para este curso

    COMUNIDAD MOOC IR AL CURSO

    *Irás a la plataforma donde se impartirá el mooc y donde podrás registrarte

    • GRATIS
    • 03-04-2017FECHA INICIO
    • Eindhoven University of Technology
    • Daniel Lakens

    Acerca de Mooc.es

    En Mooc.es encontrarás la mayor oferta de cursos de las mejores universidades del mundo.

    El conocimiento al alcance de todos.

    ¡Compártelo!

    Share on Facebook0Share on LinkedIn0Share on Google+0Tweet about this on Twitter