Statistical bias is essentially when a model or statistic is unrepresentative of the population, and there are several sources of bias that cause this.
Types of statistical bias:
1. Selection bias
Selection bias is the phenomenon of selecting individuals, groups or data for analysis in such a way that proper randomization is not achieved.
2. Survivorship bias
The phenomenon where only those that ‘survived’ a long process are included or excluded in an analysis, thus creating a biased sample.
3. Omitted variable bias
This is bias that stems from the absence of relevant variables in a model.
4. Recall bias
Recall bias is a type of information bias where participants do not ‘recall’ previous events, memories, or details.
5. Observer bias
This is the bias that stems from the subjective viewpoint of observers and how they assess subjective criteria or record subjective information.
6. Funding bias
This is the bias that stems from the subjective viewpoint of observers and how they assess subjective criteria or record subjective information.