Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences.

Christie, Alec P Abecasis, David Adjeroud, Mehdi Alonso, Juan C Amano, Tatsuya Anton, Alvaro Baldigo, Barry P Barrientos, Rafael Bicknell, Jake E Buhl, Deborah A Cebrian, Just Ceia, Ricardo S Cibils-Martina, Luciana Clarke, Sarah Claudet, Joachim Craig, Michael D Davoult, Dominique De Backer, Annelies Donovan, Mary K Eddy, Tyler D França, Filipe M Gardner, Jonathan P A Harris, Bradley P Huusko, Ari Jones, Ian L Kelaher, Brendan P Kotiaho, Janne S López-Baucells, Adrià Major, Heather L Mäki-Petäys, Aki Martín, Beatriz Martín, Carlos A Martin, Philip A Mateos-Molina, Daniel McConnaughey, Robert A Meroni, Michele Meyer, Christoph F J Mills, Kade Montefalcone, Monica Noreika, Norbertas Palacín, Carlos Pande, Anjali Pitcher, C Roland Ponce, Carlos Rinella, Matt Rocha, Ricardo Ruiz-Delgado, María C Schmitter-Soto, Juan J Shaffer, Jill A Sharma, Shailesh Sher, Anna A Stagnol, Doriane Stanley, Thomas R Stokesbury, Kevin D E Torres Moreno, Aurora [UCL] Tully, Oliver Vehanen, Teppo Watts, Corinne Zhao, Qingyuan Sutherland, William J et al.[show all]

Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.