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Much empirical science involves evaluating alternative explanations for the obtained data. For example, given certain assumptions underlying a statistical test, a "significant" result generally refers to implausibility of a null (zero) effect in the population producing the obtained study data. However, methodological work on various versions of p-hacking (i.e., using different analysis strategies until a "significant" result is produced) questions whether significant p-values might often reflect false findings. Indeed, initial simulations of single studies showed that the potential for finding "significant" but false findings might be much higher than the nominal.05 value when various analysis flexibilities are undertaken. In many settings, however, research articles report multiple studies using consistent methods across the studies, where those consistent methods would constrain the flexibilities used to produce high false-finding rates for simulations of single studies. Thus, we conducted simulations of study sets. These simulations show that consistent methods across studies (i.e., consistent in terms of which measures are analyzed, which conditions are included, and whether and how covariates are included) dramatically reduce the potential for flexible research practices (p-hacking) to produce consistent sets of significant results across studies. For p-hacking to produce even modest probabilities of a consistent set of studies would require (a) a large amount of selectivity in study reporting and (b) severe (and quite intentional) versions of p-hacking. With no more than modest selective reporting and with consistent methods across studies, p-hacking does not provide a plausible explanation for consistent empirical results across studies, especially as the size of the reported study set increases. In addition, the simulations show that p-hacking can produce high rates of false findings for single studies with very large samples. In contrast, a series of methodologically-consistent studies (even with much smaller samples) is much less vulnerable to the forms of p-hacking examined in the simulations. [ABSTRACT FROM AUTHOR]

Communicators commonly present two-sided messages to avoid being perceived as biased. This approach equates bias with one-sidedness rather than divergence from the position supported by available data. Messages often concern topics with mixed qualities: a product is exceptional but expensive; a politician is inexperienced but ethical. For these topics, providing a two-sided message should reduce perceived bias according to both views of bias as one-sidedness and divergence from available data. However, if perceived bias follows divergence from available data, for topics viewed as one-sided (univalent), a two-sided message should not reduce perceived bias. Across five studies, acknowledging two sides reduced perceived bias for novel topics. In two of the studies, two-sidedness no longer reduced perceived bias for topics viewed as univalent. This work clarifies that people conceptualize bias as a divergence from available data, not simply one-sidedness. It also clarifies when and how to leverage message-sidedness to reduce perceived bias. [ABSTRACT FROM AUTHOR]

This study investigated whether political endorsements from in‐ versus out‐group political elites would influence likelihood of COVID‐19 vaccination. In March 2021, we ran an experiment with Democrats and Republicans in the United States to examine whether they would be more likely to get vaccinated following endorsements by former Presidents Obama or Trump. Participants reported greater likelihood of getting vaccinated if the vaccine was endorsed by an elite from their own rather than the opposing party. This effect was driven by Trump, who increased vaccination likelihood among Republicans but decreased it among Democrats. We also investigated the mechanisms underlying this persuasion effect and found that perceived bias and liking were plausible mediators, whereas perceived trustworthiness and expertise were not. This study highlights the potential of having endorsements from both Democrat and Republican political elites to increase support for health behaviors in a politically charged climate. [ABSTRACT FROM AUTHOR]

Past research suggests that people continue believing retracted misinformation more when it is consistent versus inconsistent with their attitudes. However, the psychological mechanism responsible for this phenomenon remains unclear. We predicted that retractions of attitude-consistent misinformation produce greater feelings of discomfort than retractions of attitude-inconsistent misinformation and that this discomfort predicts continued belief in and use of the misinformation. We report combined analyses across 10 studies testing these predictions. Seven studies (total N = 1,323) used a mediational framework and found that the more consistent misinformation was with participants' attitudes, the more discomfort was elicited by a retraction of the misinformation. Greater discomfort then predicted greater continued belief in the misinformation, which, in turn, predicted greater use of the misinformation when participants made relevant inferences. Three additional studies (total N = 574) utilized misattribution paradigms to demonstrate that the relation between discomfort and belief in misinformation is causal in nature. [ABSTRACT FROM AUTHOR]