Prof. Dr. Felix Schönbrodt
In 2021, the German Psychological Society (DGPs) signed the DORA declaration. In consequence, they recently installed a task force with the goal to create a recommendation how a responsible research assessment could be practically implemented in hiring and promotion within Continue Reading …
by Angelika Stefan & Felix Schönbrodt Almost all researchers have experienced the tingling feeling of suspense that arises right before they take a look at long-awaited data: Will they support their favored hypothesis? Will they yield interesting or even groundbreaking Continue Reading …
tl;dr: Publication bias and p-hacking can dramatically inflate effect size estimates in meta-analyses. Many methods have been proposed to correct for such bias and to estimate the underlying true effect. In a large simulation study, we studied which methods do Continue Reading …
by Angelika Stefan & Felix Schönbrodt This is the second part of “Two meanings of priors”. The first part explained a first meaning – “priors as subjective probabilities of models”. While the first meaning of priors refers to a global Continue Reading …
by Angelika Stefan & Felix Schönbrodt When reading about Bayesian statistics, you regularly come across terms like “objective priors“, “prior odds”, “prior distribution”, and “normal prior”. However, it may not be intuitively clear that the meaning of “prior” differs in Continue Reading …
Democratizing p-hacking: Leveling the playing field by teaching p-hacking to students and early career researchers. Continue Reading …
tl;dr: Optional stopping does not bias parameter estimates from a frequentist point of view if all studies are reported (i.e., no publication bias exists) and effect sizes are appropriately meta-analytically weighted. Several recent discussions on the Psychological Methods Facebook group Continue Reading …
If the p-value is < .05, then the probability of falsely rejecting the null hypothesis is <5%, right? That means, a maximum of 5% of all significant results is a false-positive (that’s what we control with the α rate). Well, Continue Reading …
[This is a guest post by Eric-Jan Wagenmakers and Quentin Gronau introducing the RGraphCompendium. Click here to see the full compendium!] Every data analyst knows that a good graph is worth a thousand words, and perhaps a hundred tables. But Continue Reading …
A Bayes factor (BF) is a statistical index that quantifies the evidence for a hypothesis, compared to an alternative hypothesis (for introductions to Bayes factors, see here, here or here). Although the BF is a continuous measure of evidence, humans Continue Reading …