Ageism has become a social problem in an aged society. This study re-examines an ageism affirmation strategy; the designs and plans for this study were pre-registered. Participants were randomly assigned to either an experimental group (in which they read an explanatory text about the stereotype embodiment theory and related empirical findings) or a control group (in which they read an irrelevant text). The hypothesis was that negative attitudes toward older adults are reduced in the experimental group compared with the control group. Bayesian analysis was used for hypothesis testing. The results showed that negative attitudes toward older adults were reduced in the experimental group. These findings contribute to the development of psychological and gerontological interventions aimed at affirming ageism. In addition, continued efforts to reduce questionable research practices and the spread of Bayesian analysis in psychological research are expected.

We surveyed 348 Psychology and Education researchers within Spain, on issues such as their perception of a crisis in Science, their confidence in the quality of published results, and the use of questionable research practices (QRP). Their perceptions regarding pressure to publish and academic competition were also collected. The results indicate that a large proportion of the sample of Spanish academics think there is a crisis in Science, mainly due to a lack of economic investment, and doubts the quality of published findings. They also feel strong pressure to publish in high impact factor journals and a highly competitive work climate.

Criticism of null hypothesis significance testing, confidence intervals, and frequentist statistics in general has evolved into advocacy of Bayesian analyses with informative priors for strong inference. This paper shows that Bayesian analysis with informative priors is formally equivalent to data falsification because the information carried by the prior can be expressed as the addition of fabricated observations whose statistical characteristics are determined by the parameters of the prior. This property of informative priors makes clear that only the use of non-informative, uniform priors in all types of Bayesian analyses is compatible with standards of research integrity. At the same time, though, Bayesian estimation with uniform priors yields point and interval estimates that are identical or nearly identical to those obtained with frequentist methods. At a qualitative level, frequentist and Bayesian outcomes have different interpretations but they are interchangeable when uniform priors are used. Yet, Bayesian interpretations require either the assumption that population parameters are random variables (which they are not) or an explicit acknowledgment that the posterior distribution (which is thus identical to the likelihood function except for a scale factor) only expresses the researcher’s beliefs and not any information about the parameter of concern.