Water quality agencies and scientists are increasingly adopting standardized sampling methodologies because of the challenges associated with interpreting data derived from dissimilar protocols. Here, we compare 13 protocols for monitoring streams from different regions and countries around the globe. Despite the spatially diverse range of countries assessed, many aspects of bioassessment structure and protocols were similar, thereby providing evidence of key characteristics that might be incorporated in a global sampling methodology. Similarities were found regarding sampler type, mesh size, sampling period, subsampling methods, and taxonomic resolution. Consistent field and laboratory methods are essential for merging data sets collected by multiple institutions to enable large-scale comparisons. We discuss the similarities and differences among protocols and present current trends and future recommendations for monitoring programs, especially for regions where large-scale protocols do not yet exist. We summarize the current state in one of these regions, Latin America, and comment on the possible development path for these techniques in this region. We conclude that several aspects of stream biomonitoring need additional performance evaluation (accuracy, precision, discriminatory power, relative costs), particularly when comparing targeted habitat (only the commonest habitat type) versus site-wide sampling (multiple habitat types), appropriate levels of sampling and processing effort, and standardized indicators to resolve dissimilarities among biomonitoring methods. Global issues such as climate change are creating an environment where there is an increasing need to have universally consistent data collection, processing and storage to enable large-scale trend analysis. Biomonitoring programs following standardized methods could aid international data sharing and interpretation.

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Sequential testing has been employed in clinical assessments to support student progression decisions by strategically targeting assessment resources towards borderline students. In this context resampling techniques have been utilised in the attempt to determine the appropriate blueprint number of stations to include in the screening phase of a sequential exam. However, statistical overfitting undermines the generalizability of examination psychometric properties and the uneven distribution (imbalance) of borderline vs. non-borderline students may cause resampling methods to produce biased results. Both phenomena may mislead educational practitioners when redesigning sequential assessments. We demonstrate how to mitigate against the problems of overfitting and imbalanced cohorts whilst finding the optimal 9 screening stations out of an 18-station OSCE. To prevent overfitting our statistical model was developed on one set of data (train and test) and then validated on a different dataset (validation) with imbalance accounted for by operating a stratified sampling scheme. The outcomes demonstrate the importance of validation: in the development phase, the accuracy was initially 91% (train) but the actual predictive accuracy when mitigating against overfitting and imbalance was 86% (test). Similarly, when we validated the model on completely new data-with a comparable assessment-the predictive accuracy was 83% (validation).