bias
26
5.8
- noun
- /ˈbaɪəs/
- Specialized
:
sesgo
- They say data bias in a world designed for men.
- selection bias
- potential bias
- minimize bias
Could this be the result of some systematic bias in school identification procedures?
- Could this be the result of some systematic bias in school identification procedures?
- And then from there you like replace the bias, like bias replacement.
- That algorithms have too much biases or have too many biases.
- And so there's a bias in choosing a family of, of models.
- And that is like the negative bias, survival bias language.
- So what about the the possibility of bias introduced?
Examples
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To correct for this sampling bias, CCCSE calculates an enrollment weight that allows for representative descriptive statistics.
Academic text (2011) -
The researchers acknowledged the presence of bias in their data, which could lead to unfair results.
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It's that home bias that that Harry talked about earlier.
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One surprising finding is that the technical bias for energy appears to be positive.
Academic text (1992) -
In case the returns were just sector bias, the results were robust.
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All the necessary steps were taken to prevent any claims of researcher bias or improper testing methods.
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Risk of bias is assessed on a three-point scale: high bias, low bias, and unclear.
Academic text (2017) -
Because of survivorship bias, the good ideas persist.
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Problems with the method include issues of scale, bias, limited datasets, and quantification.
Academic text (2013) -
The model showed an undercounting bias in the 1950s and the mid-1960s.
Academic text (2011)
Synonyms
5
7.8
Unfair treatment built into a computer program or information that harms certain groups
3
6.7
An unfair liking that makes you treat one person or group better than others
Antonyms
Compounds
- Specialized
A researcher's expectations change how participants act in a study and affect results
How Biased
- Specialized
26
5.8
236
1.9
Surface Forms
Etymology
Bias in computing keeps the old idea of a 'slant': it is a systematic 'tilt' or error in data or a model that makes results unfair for some people.