For Yet Better Science

Engineering organisms leading to the restoration of extinct species like dodos and dinosaurs is an impulsive idea with less clarity on its potential benefits.  

When science is viewed in isolation from the past and politics, it’s easier for those with bad intentions to revive dangerous and discredited ideas.  

At the heart of the Open Science movement is the conviction that  Open Science is better science . More rigorous. More inclusive. More efficient. More trustworthy. More reproducible. And more impactful for society.  

The “New Statistics” emphasizes effect sizes, confidence intervals, meta-analysis, and the use of Open Science practices. We present three specific ways in which a New Statistics approach can help improve scientific practice: by reducing overconfidence in small samples, by reducing confirmation bias, and by fostering more cautious judgments of consistency. We illustrate these points through consideration of the literature on oxytocin and human trust, a research area that typifies some of the endemic problems that arise with poor statistical practice.  

I often like to discuss science online and I’m also rather partial to topics that promote lively discussion, such as  climate change ,  crime statistics  and (perhaps surprisingly) the  big bang . This inevitably brings out the  trolls .  

To understand disease, scientists are producing comprehensive  omics  datasets. However, the majority of these are Eurocentric. Recently, the inclusion of patients from Asia and the Middle East in genomic analyses uncovered unique loci linked to COVID-19 severity. This demonstrates that focusing on diversity and underrepresented populations can benefit all.  

Conducting research with human subjects can be difficult because of limited sample sizes and small empirical effects. We demonstrate that this problem can yield patterns of results that are practically indistinguishable from flipping a coin to determine the direction of treatment effects. We use this idea of random conclusions to establish a baseline for interpreting effect-size estimates, in turn producing more stringent thresholds for hypothesis testing and for statistical-power calculations. An examination of recent meta-analyses in psychology, neuroscience, and medicine confirms that, even if all considered effects are real, results involving small effects are indeed indistinguishable from random conclusions.