Bias in Statistics > Observer Bias (Research or Experimenter Bias)
What is Observer Bias?
Observer bias (also called experimenter bias or research bias) is the tendency to see what we expect to see, or what we want to see. When a researcher studies a certain group, they usually come to an experiment with prior knowledge and subjective feelings about the group being studied. In other words, they come to the table with conscious or unconscious prejudices.
Observer bias can be reduced or eliminated by:
- Ensuring that observers are well trained.
- Screening observers for potential biases.
- Having clear rules and procedures in place for the experiment.
- Making sure behaviors are clearly defined.
- Setting a time frame for: collecting data, for the duration of the experiment, and for experimental parts.
Real Life Example of Experimenter Bias
One famous example of observer bias is the work of Cyril Burt, a psychologist best known for his work on the heritability of IQ. He thought that children from families with a low socioeconomic status (i.e. working class children) were also more likely to have lower intelligence, compared to children from higher socioeconomic statuses (Fancher, 1985). His “scientific” approach to intelligence testing was revolutionary, and “proved” that children from the working classes were in general, less intelligent. This led to the creation of a two-tier educational system in England in 1960s which sent middle and upper class children to elite schools and working class children to less desirable schools. Burt’s research was later debunked and it was concluded he falsified data. It is now accepted that intelligence is not hereditary.
“Burt’s crime is the very plausibility of his fiction
which was manufactured to feed his, and our prejudices… for heritability (Esling, 1982, cited in The Case of Cyril Burt).”
Observer bias is also known as:
- Expectancy bias,
- Experimenter effect / Experimenter Bias,
- Experimenter-expectancy effect,
- Information Bias,
- Observer effect,
- Observer-expectancy effect,
- Research Bias.
Esling (1982) concludes,
Fancher, R.E. (1985). The intelligence men: Makers of the IQ controversy. New York: W.W. Norton & Company.
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.Comments are now closed for this post. Need help or want to post a correction? Please post a comment on our Facebook page and I'll do my best to help!