Related Experiment Video
Updated: Feb 2, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
Published on: February 23, 2024
A literature review of the predictive validity of European dental school selection methods
Claudia Cunningham1, Fiona Patterson2, Jennifer Cleland3
1Aberdeen Dental Institute, University of Aberdeen, Aberdeen, UK.
Introduction:
Selection to dental school is the point at which there is the potential to assess a wide range of candidate attributes and select those most likely to learn, train and work within the profession. Despite this, little is known in terms of what works and what does not work in dental selection in terms of predicting future performance accurately and fairly. Given this, our aim was to synthesise the last 30 years of research investigating the predictive validity of dental school selection methods.
Methods:
A search of the electronic databases SCOPUS, Pubmed and Embase was conducted. Results were limited to English language studies published between January 1987 and January 2017.
Results:
Twenty-one studies were included. Selection tools fell into five broad categories: tests of personal qualities; cognitive ability; academic attainment; psychomotor skills and combined ability tests. Most were retrospective, single-site studies limited to early years of dental school. Weak correlations were reported, but in most cases, these were between small sections of the selection tool and/or the outcome measure.
Discussion:
There was a notable dearth of published research examining dental schools selection processes across Europe over the last 30 years. Current literature was limited by weak study design and lack of long-term follow-up.
Conclusion:
There is insufficient high-quality evidence from which to draw any conclusions as to the best selection methods to use in dental school selection. Without this, designing selection frameworks for dentistry which are appropriately weighted, reliable and valid remains a challenge.
More Related Videos
Related Concept Videos
Reliability and Validity
Review and Preview
Percentiles are a type of fractile that partition data into...
Review and Preview
Predicting Molecular Geometry
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Data Validation
Key parameters for method validation include:

