Towards a unified approach to formal “risk of bias” assessments for causal and descriptive inference
UK Centre for Ecology & Hydrology · Newcastle University
Abstract
Abstract Statistics is sometimes described as the science of reasoning under uncertainty. Statistical models provide one view of this uncertainty, but what is frequently neglected is the “invisible” portion of uncertainty: that assumed not to exist once a model has been fitted to some data. Systematic errors, i.e. bias, in data relative to some model and inferential goal can seriously undermine research conclusions, and qualitative and quantitative techniques have been created across several disciplines to quantify and generally appraise such potential biases. Perhaps best known are so-called “risk of bias” assessment instruments used to investigate the likely quality of randomised controlled trials in medical…
Citation impact
- FWCI
- 0.00
- Percentile
- 96%
- References
- 88
Authors
4Topics & keywords
- Statistical inference
- Causal inference
- Inference
- Econometrics
- Computer science
- Sampling (signal processing)
- Statistical model
- Causal model