Bias (statistics): Difference between revisions

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	In ], the term '''bias''' is used for describing several different concepts:		In ], the term '''bias''' is used for describing several different concepts:
	*], where there is an error in choosing the individuals or groups to take part in a ].		*], where there is an error in choosing the individuals or groups to take part in a ].
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Revision as of 17:41, 28 February 2010

In statistics, the term bias is used for describing several different concepts:

Selection bias, where there is an error in choosing the individuals or groups to take part in a scientific study.
- A biased sample, sometimes classified as a result of selection bias, is one in which some members of the population are more likely to be included than others.
  - Spectrum bias consists of evaluating the ability of a diagnostic test in a biased group of patients, which leads to an overestimate of the sensitivity and specificity of the test.
The bias of an estimator is the difference between an estimator's expectation and the true value of the parameter being estimated.
- Omitted-variable bias is the bias that appears in estimates of parameters in a regression analysis when the assumed specification is incorrect, in that it omits an independent variable that should be in the model.
In statistical hypothesis testing, a test is said to be unbiased when the probability of rejecting the null hypothesis exceeds the significance level when the alternative is true and is less than or equal to the significance level when the null hypothesis is true.
Systematic bias or systemic bias are external influences that may affect the accuracy of statistical measurements.
Data-snooping bias comes from the misuse of data mining techniques.

Topics referred to by the same term

This disambiguation page lists statistics articles associated with the title Bias.
If an internal link led you here, you may wish to change the link to point directly to the intended article.

v t e Biases
Cognitive biases	Acquiescence Ambiguity Affinity Anchoring Attentional Attribution Actor–observer Correspondence Authority Automation Availability Mean world Belief Blind spot Choice-supportive Commitment Confirmation Selective perception Compassion fade Congruence Cultural Declinism Distinction Dunning–Kruger Egocentric Curse of knowledge Emotional Extrinsic incentives Fading affect Framing Frequency Frog pond effect Halo effect Hindsight Horn effect Hostile attribution Impact Implicit In-group Intentionality Illusion of transparency Mean world syndrome Mere-exposure effect Narrative Negativity Normalcy Omission Optimism Out-group homogeneity Outcome Overton window Precision Present Pro-innovation Proximity Response Restraint Self-serving Social comparison Social influence bias Spotlight Status quo Substitution Time-saving Trait ascription Turkey illusion von Restorff effect Zero-risk In animals
Statistical biases	Estimator Forecast Healthy user Information Psychological Lead time Length time Non-response Observer Omitted-variable Participation Recall Sampling Selection Self-selection Social desirability Spectrum Survivorship Systematic error Systemic Verification Wet
Other biases	Academic Basking in reflected glory Déformation professionnelle Funding FUTON Inductive Infrastructure Inherent In education Liking gap Media False balance Vietnam War Norway South Asia Sweden United States Arab–Israeli conflict Ukraine Net Political bias Publication Reporting White hat
Bias reduction	Cognitive bias mitigation Debiasing Heuristics in judgment and decision-making
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