Open Science: What, Why, and How?

About

The goal of scientific research is to accumulate knowledge. Researchers generate hypotheses and collect data to investigate whether or not empirical observations are consistent with these hypotheses. However, even though science aspires towards accuracy in this process, errors are inevitable. A fundamental characteristic that sets empirical science apart from other sources of knowledge is the ability to self-correct; any empirical observation is subject to validation and may be shown to be wrong. The reproducibility of empirical results constitutes a cornerstone of the scientific method. As a consequence of accumulative evidence emphasizing low levels of replicability, there is increasing concern that a considerable fraction—or even a majority—of published research claims may be false. The drivers of this „credibility crisis“ are manifold: the file drawer effect, insufficient statistical power, publication bias, confirmation bias, dodgy incentives in the publication process, p-hacking, etc.

The summer school aims to provide a critical view on the “rules of a game named science” and provides an introduction to remedies to the manifold issues jeopardizing the credibility of scientific results: power calculations, confirmatory research (pre-registration), and open and transparent research practices.

Course leader

Felix Holzmeister, PhD Ass.-Prof. of Behavioral and Experimental Economics and Finance University of Innsbruck, Department of Economics web: www.holzmeister.biz mail: felix.holzmeister@uibk.ac.at

Target group

PhD students from all disciplines; early career researchers (PreDocs, PostDocs); graduates interested in open science

Course aim

Upon successful completion of the summer school, students are expected to: (i) understand why and how common (mal)practices in scholarly research translate into low levels of replicability and low reliability; (ii) understand the virtue of confirmatory research and transparent research practices regarding the credibility of research findings; (iii) establish a thorough understanding of the statistical concepts related to hypothesis testing (error rates, significance, power, etc.); (iv) be able to undertake a priori power calculations and sensitivity analysis, and to devise pre-analysis plans for research projects; and (v) be able to critically assess scientific projects and results with respect to malpractices, research integrity, and ethical aspects.

Fee info