Resources

The credibility of psychological findings can be undermined by a history of questionable research practices (QRPs) by researchers. One remedy for this problem is the pre-registration of a study in which a research protocol is registered before …

We surveyed 807 researchers (494 ecologists and 313 evolutionary biologists) about their use of Questionable Research Practices (QRPs), including cherry picking statistically significant results, p hacking, and hypothesising after the results are …

The current discussion of questionable research practices (QRPs) is meant to improve the quality of science. It is, however, important to conduct QRP studies with the same scrutiny as all research. We note problems with overestimates of QRP …

This lesson in part of Software Carpentry workshop and teach novice programmers to write modular code and best practices for using R for data analysis. an introduction to R for non-programmers using gapminder data The goal of this lesson is to teach …

Data Carpentry lesson part of the Social Sciences curriculum. This lesson teaches how to analyse and visualise data used by social scientists. Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so …

This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your …

Una introducción a R utilizando los datos de Gapminder. El objetivo de esta lección es enseñar a las programadoras principiantes a escribir códigos modulares y adoptar buenas prácticas en el uso de R para el análisis de datos. R nos provee un …

This paper investigates how competition to publish first and thereby establish priority impacts the quality of scientific research. We begin by developing a model where scientists decide whether and how long to work on a given project. When deciding …

Hypothesizing after the results are known (HARK) has been disparaged as data dredging, and safeguards including hypothesis preregistration and statistically rigorous oversight have been recommended. Despite potential drawbacks, HARK has deepened …

C. Glenn Begley and Lee M. Ellis propose how methods, publications and incentives must change if patients are to benefit.