Have psychologists increased reporting of outliers in response to the reproducibility crisis?

Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality

With over 10,000 new reaction protocols arising every year, only a handful of these procedures transition from academia to application. A major reason for this gap stems from the lack of comprehensive knowledge about a reaction's scope, i.e., to which substrates the protocol can or cannot be applied. Even though chemists invest substantial effort to assess the scope of new protocols, the resulting scope tables involve significant biases, reducing their expressiveness. Herein we report a standardized substrate selection strategy designed to mitigate these biases and evaluate the applicability, as well as the limits, of any chemical reaction. Unsupervised learning is utilized to map the chemical space of industrially relevant molecules. Subsequently, potential substrate candidates are projected onto this universal map, enabling the selection of a structurally diverse set of substrates with optimal relevance and coverage. By testing our methodology on different chemical reactions, we were able to demonstrate its effectiveness in finding general reactivity trends by using a few highly representative examples. The developed methodology empowers chemists to showcase the unbiased applicability of novel methodologies, facilitating their practical applications. We hope that this work will trigger interdisciplinary discussions about biases in synthetic chemistry, leading to improved data quality.

Exploring the multiverse of analysis options for the alcohol Stroop

The alcohol Stroop is a widely used task in addiction science to measure the theoretical concept of attentional bias (a selective attention to alcohol-related cues in the environment), which is thought to be associated with clinical outcomes (craving and consumption). However, recent research suggests findings from this task can be equivocal. This may be because the task has many different potential analysis pipelines, which increase researcher degrees of freedom when analysing data and reporting results. These analysis pipelines largely come from how outlying reaction times on the task are identified and handled (e.g. individual reaction times > 3 standard deviations from the mean are removed from the distribution; removal of all participant data if > 25% errors are made). We used specification curve analysis across two alcohol Stroop datasets using alcohol-related stimuli (one published and one novel) to examine the robustness of the alcohol Stroop effect to different analytical decisions. We used a prior review of this research area to identify 27 unique analysis pipelines. Across both data sets, the pattern of results was similar. The alcohol Stroop effect was present and largely robust to different analysis pipelines. Increased variability in the Stroop effect was observed when implementing outlier cut-offs for individual reaction times, rather than the removal of participants. Stricter outlier thresholds tended to reduce the size of the Stroop interference effect. These specification curve analyses are the first to examine the robustness of the alcohol Stroop to different analysis strategies, and we encourage researchers to adopt such analytical methods to increase confidence in their inferences across cognitive and addiction science.