Bias (statistics): Difference between revisions

Browse history interactively ← Previous edit Next edit →Content deleted Content addedVisual WikitextInline

Revision as of 13:40, 23 September 2010 editEmble64 (talk \| contribs)269 edits Adding funding bias (came from modification of the main bias page)← Previous edit		Revision as of 01:44, 18 November 2010 edit undo130.243.96.180 (talk)No edit summaryNext edit →
Line 1:		Line 1:
	In ], the term '''bias''' refers to several different concepts:		In ], the term '''bias''' refers to several different concepts:
	*], where individuals or groups are more likely to take part in a ] project than others, resulting in ]s. This can also be termed ''Berksonian bias''<ref>Rothman, K.J. ''et al.'' (2008) Modern epidemiology. ''Lippincott Williams & Wilkins'' pp.134-137.</ref>.		*], where individuals or groups are more likely to take part in a ] project than others, resulting in ]s. This can also be termed ''Berksonian bias''<ref>Rothman, K.J. ''et al.'' (2008) Modern epidemiology. ''Lippincott Williams & Wilkins'' pp.134-137.</ref>.
	**] arises from evaluating diagnostic tests on biased patient samples, leading to an overestimate of the sensitivity and specificity of the test.		**] arises from evaluating diagnostic tests on biased patient samples, leading to an overestimate of the ] and ] of the test.
	* The ] is the difference between an estimator's expectation and the true value of the parameter being estimated.		* The ] is the difference between an estimator's expectation and the true value of the parameter being estimated.
	** ] 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.		** ] 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.

Revision as of 01:44, 18 November 2010

In statistics, the term bias refers to several different concepts:

Selection bias, where individuals or groups are more likely to take part in a research project than others, resulting in biased samples. This can also be termed Berksonian bias.
- Spectrum bias arises from evaluating diagnostic tests on biased patient samples, leading 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.
- Detection bias is where a phenomenon is more likely to be observed and/or reported for a particular set of study subjects. For instance, the syndemic involving obesity and diabetes may mean doctors are more likely to look for diabetes in obese patients than in less overweight patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.
- Funding bias may lead to selection of outcomes, test samples, or test procedures that favor a study's financial sponsor.
- Reporting bias involves a skew in the availability of data, such that observations of a certain kind may be more likely to be reported and consequently used in research.
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
Lists: General Memory

References

Rothman, K.J. et al. (2008) Modern epidemiology. Lippincott Williams & Wilkins pp.134-137.

Categories: