Related Experiment Video
Updated: Jul 6, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
Published on: November 22, 2019
Modelling of errors in databases
Steve Gallivan1, Christina Pagel
1Clinical Operational Research Unit, University College London, Gower Street, London WC1H 0BT, UK. steve.gallivan@ucl.ac.uk
Abstract:
A lot of time and energy are expended assembling national databases containing information about health care processes and outcomes. Unfortunately, given the complexity of the data gathering procedures involved, errors occur. This inevitably leads to problems when it comes to the analysis of data from such sources. Indeed, sometimes it is very much a matter of faith that summary statistics represent a true reflection of the facts. On the assumption that one knows the rates at which different forms of errors occur, mathematical modelling methods can be used to obtain estimates of the effects of such errors on the estimates that would be derived for summary statistics associated with an erroneous data base.
Related Concept Videos
Types of Errors: Detection and Minimization
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Random and Systematic Errors
Random and Systematic Errors
Random Error
Errors and Mistakes in Surveying
