Resources

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 …

Pre-Analysis Plans (PAPs) for randomized evaluations are becoming increasingly common in Economics, but their definition remains unclear and their practical applications therefore vary widely. Based on our collective experiences as researchers and …

Jacob Cohen (see record 1995-12080-001) raised a number of questions about the logic and information value of the null hypothesis statistical test (NHST). Specifically, he suggested that: (1) The NHST does not tell us what we want to know; (2) the …

Much has been written regarding p-values below certain thresholds (most notably 0.05) denoting statistical significance and the tendency of such p-values to be more readily publishable in peer-reviewed journals. Intuition suggests that there may be a …

During multiple testing, researchers often adjust their alpha level to control the familywise error rate for a statistical inference about a joint union alternative hypothesis (e.g., “H1,1 or H1,2”). However, in some cases, they do not make this …

Ecological momentary assessment (EMA) represents a promising approach to study cognitive aging. In contrast to laboratory-based studies, EMA involves the repeated sampling of experiences in daily life contexts, enabling investigators to gain access …

Despite the potential benefits of sequential designs, studies evaluating treatments or experimental manipulations in preclinical experimental biomedicine almost exclusively use classical block designs. Our aim with this article is to bring the …

This paper presents a generalized registration form for systematic reviews that can be used when currently available forms are not adequate. The form is designed to be applicable across disciplines (i.e., psychology, economics, law, physics, or any …

Empirical research inevitably includes constructing a data set by processing raw data into a form ready for statistical analysis. Data processing often involves choices among several reasonable options for excluding, transforming, and coding data. We …

Conservation science is a multidisciplinary and collaborative discipline. This journal's policy is to encourage transparent and open practices in science, including sharing of data, code, and survey instruments. Such practices are especially …