107:. For example, a survey of high school students to measure teenage use of illegal drugs will be a biased sample because it does not include home-schooled students or dropouts. A sample is also biased if certain members are underrepresented or overrepresented relative to others in the population. For example, a "man on the street" interview which selects people who walk by a certain location is going to have an overrepresentation of healthy individuals who are more likely to be out of the home than individuals with a chronic illness. This may be an extreme form of biased sampling, because certain members of the population are totally excluded from the sample (that is, they have zero probability of being selected).
497:, as they stem from the same chemical process(es). These correlations depend on space (i.e., location) and time (i.e., period). Therefore, a pollutant distribution is not necessarily representative for every location and every period. If a low-cost measurement instrument is calibrated with field data in a multivariate manner, more precisely by collocation next to a reference instrument, the relationships between the different compounds are incorporated into the calibration model. By relocation of the measurement instrument, erroneous results can be produced.
546:
365:. In statistical usage, bias merely represents a mathematical property, no matter if it is deliberate or unconscious or due to imperfections in the instruments used for observation. While some individuals might deliberately use a biased sample to produce misleading results, more often, a biased sample is just a reflection of the difficulty in obtaining a truly representative sample, or ignorance of the bias in their process of measurement or analysis. An example of how ignorance of a bias can exist is in the widespread use of a ratio (a.k.a.
369:) as a measure of difference in biology. Because it is easier to achieve a large ratio with two small numbers with a given difference, and relatively more difficult to achieve a large ratio with two large numbers with a larger difference, large significant differences may be missed when comparing relatively large numeric measurements. Some have called this a 'demarcation bias' because the use of a ratio (division) instead of a difference (subtraction) removes the results of the analysis from science into pseudoscience (See
128:, which are biased samples because the respondents are self-selected. Those individuals who are highly motivated to respond, typically individuals who have strong opinions, are overrepresented, and individuals that are indifferent or apathetic are less likely to respond. This often leads to a polarization of responses with extreme perspectives being given a disproportionate weight in the summary. As a result, these types of polls are regarded as unscientific.
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380:, for example, deliberately oversamples from minority populations in many of its nationwide surveys in order to gain sufficient precision for estimates within these groups. These surveys require the use of sample weights (see later on) to produce proper estimates across all ethnic groups. Provided that certain conditions are met (chiefly that the weights are calculated and used correctly) these samples permit accurate estimation of population parameters.
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422:, by a large margin. The result was the exact opposite. The Literary Digest survey represented a sample collected from readers of the magazine, supplemented by records of registered automobile owners and telephone users. This sample included an over-representation of wealthy individuals, who, as a group, were more likely to vote for the Republican candidate. In contrast, a poll of only 50 thousand citizens selected by
267:. In a perfect world we should be able to discover all such families with a gene including those who are simply carriers. In this situation the analysis would be free from ascertainment bias and the pedigrees would be under "nontruncate selection" In practice, most studies identify, and include, families in a study based upon them having affected individuals.
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If entire segments of the population are excluded from a sample, then there are no adjustments that can produce estimates that are representative of the entire population. But if some groups are underrepresented and the degree of underrepresentation can be quantified, then sample weights can correct
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in the population. Sampling bias occurs in practice as it is practically impossible to ensure perfect randomness in sampling. If the degree of misrepresentation is small, then the sample can be treated as a reasonable approximation to a random sample. Also, if the sample does not differ markedly in
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require for many real-time and some longitudinal forms of study). Participants' decision to participate may be correlated with traits that affect the study, making the participants a non-representative sample. For example, people who have strong opinions or substantial knowledge may be more willing
257:). In this case their children will each have a 25% chance of showing the characteristic. The problem arises because we can't tell which families have both parents as carriers (heterozygous) unless they have a child who exhibits the characteristic. The description follows the textbook by Sutton.
277:
have an equal chance of being included in a study this is called truncate selection, signifying the inadvertent exclusion (truncation) of families who are carriers for a gene. Because selection is performed on the individual level, families with two or more affected children would have a higher
459:. Survey research was then in its infancy, and few academics realized that a sample of telephone users was not representative of the general population. Telephones were not yet widespread, and those who had them tended to be prosperous and have stable addresses. (In many cities, the
153:, when the study population is likely healthier than the general population. For example, someone in poor health is unlikely to have a job as manual laborer, so if a study is conducted on manual laborers, the health of the general population will likely be overestimated.
229:, parents try to prevent their children from being stigmatized with those diagnoses, introducing further bias. Studies carefully selected from whole populations are showing that many conditions are much more common and usually much milder than formerly believed.
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of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected. If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of
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of 2.5 for each male and 0.625 for each female. This would adjust any estimates to achieve the same expected value as a sample that included exactly 50 men and 50 women, unless men and women differed in their likelihood of taking part in the survey.
189:, in which only "surviving" subjects are selected, ignoring those that fell out of view. For example, using the record of current companies as an indicator of business climate or economy ignores the businesses that failed and no longer exist.
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into the study area (this may occur when newcomers are not available in a register used to identify the source population). Excluding subjects who move out of the study area during follow-up is rather equivalent of dropout or nonresponse, a
337:, etc. are most likely to remain intact to the modern era in caves. Prehistoric people are associated with caves because that is where the data still exists, not necessarily because most of them lived in caves for most of their lives.
90:
for differences or similarities found in the sample at hand. In this sense, errors occurring in the process of gathering the sample or cohort cause sampling bias, while errors in any process thereafter cause selection bias.
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The study of medical conditions begins with anecdotal reports. By their nature, such reports only include those referred for diagnosis and treatment. A child who can't function in school is more likely to be diagnosed with
161:, when the study population is selected from a hospital and so is less healthy than the general population. This can result in a spurious negative correlation between diseases: a hospital patient without diabetes is
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For example, a hypothetical population might include 10 million men and 10 million women. Suppose that a biased sample of 100 patients included 20 men and 80 women. A researcher could correct for this imbalance by
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The probabilities of each of the families being selected is given in the figure, with the sample frequency of affected children also given. In this simple case, the researcher will look for a frequency of
737:
245:
Geneticists are limited in how they can obtain data from human populations. As an example, consider a human characteristic. We are interested in deciding if the characteristic is inherited as a
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made nearly 40,000 years ago. If there had been contemporary paintings on trees, animal skins or hillsides, they would have been washed away long ago. Similarly, evidence of fire pits,
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the bias. However, the success of the correction is limited to the selection model chosen. If certain variables are missing the methods used to correct the bias could be inaccurate.
752:
207:, the uncritical assumption that all members or cases of a certain class or type are like those that receive the most attention or coverage in the media.
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than a child who struggles but passes. A child examined for one condition is more likely to be tested for and diagnosed with other conditions, skewing
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that actually is a result of the exposure. The control group becomes more similar to the cases in regard to exposure than does the general population.
1947:
404:
A classic example of a biased sample and the misleading results it produced occurred in 1936. In the early days of opinion polling, the
American
455:, was photographed holding a newspaper bearing this headline. The reason the Tribune was mistaken is that their editor trusted the results of a
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78:, but some classify it as a separate type of bias. A distinction, albeit not universally accepted, of sampling bias is that it undermines the
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119:), which is possible whenever the group of people being studied has any form of control over whether to participate (as current standards of
1922:
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computed of the sample is systematically erroneous. Sampling bias can lead to a systematic over- or under-estimation of the corresponding
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253:, if the parents in a family do not have the characteristic, but carry the allele for it, they are carriers (e.g. a non-expressive
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470:). In addition, the Gallup poll that the Tribune based its headline on was over two weeks old at the time of the printing.
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62:. Ascertainment bias has basically the same definition, but is still sometimes classified as a separate type of bias.
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1296:
Cuddeback G, Wilson E, Orme JG, Combs-Orme T (2004). "Detecting and
Statistically Correcting Sample Selection Bias".
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The figure shows the pedigrees of all the possible families with two children when the parents are carriers (Aa).
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197:, an effect in observational astronomy which leads to the preferential detection of intrinsically bright objects.
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1173:"The Relocation Problem of Field Calibrated Low-Cost Sensor Systems in Air Quality Monitoring: A Sampling Bias"
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bias results from exclusion of particular groups from the sample, e.g. exclusion of subjects who have recently
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Some samples use a biased statistical design which nevertheless allows the estimation of parameters. The U.S.
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has a strong negative connotation. Indeed, biases sometimes come from deliberate intent to mislead or other
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magazine collected over two million postal surveys and predicted that the
Republican candidate in the
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Ward D (20 April 2020). Sampling Bias: Explaining Wide
Variations in COVID-19 Case Fatality Rates.
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Selection and linkage desequilibrium tests under complex demographies and ascertainment bias
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An example of selection bias is called the "caveman effect". Much of our understanding of
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of a test (the ability of its results to be generalized to the entire population), while
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Berk RA (June 1983). "An
Introduction to Sample Selection Bias in Sociological Data".
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the quantity being measured, then a biased sample can still be a reasonable estimate.
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839:"The effects of sample selection bias on racial differences in child abuse reporting"
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426:'s organization successfully predicted the result, leading to the popularity of the
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test. Due to the nature of the test, the sample consisted mostly of web developers.
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952:"Domain adaptation and sample bias correction theory and algorithm for regression"
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in which a sample is collected in such a way that some members of the intended
1255:"Using excess deaths and testing statistics to determine COVID-19 mortalities"
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statistics. As certain diagnoses become associated with behavior problems or
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with an affected child has an equal chance of being selected for the study.
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to spend time answering a survey than those who do not. Another example is
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for the characteristic, depending on the type of truncate selection used.
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However, selection bias and sampling bias are often used synonymously.
27:
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19:"Spotlight fallacy" redirects here. For the psychological effect, see
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DNA fingerprinting in plants: principles, methods, and applications
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447:, which turned out to be mistaken. In the morning the grinning
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Example of biased sample: as of June 2008 55% of web browsers (
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in that it rather affects the internal validity of the study.
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Sampling bias is problematic because it is possible that a
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232:
1024:
Maxcy-Rosenau-Last Public Health and
Preventive Medicine
1027:(15th ed.). McGraw Hill Professional. p. 21.
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have been shown to account for wide variations in both
722:(Ph.D. thesis). Universitat Pompeu Fabra. p. 34.
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70:
Sampling bias is usually classified as a subtype of
58:
Medical sources sometimes refer to sampling bias as
878:Cortes C, Mohri M, Riley M, Rostamizadeh A (2008).
1000:. Lippincott Williams & Wilkins. p. 262.
65:
712:
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340:
943:
2176:Affirmative conclusion from a negative premise
1113:. National Center for Health Statistics. 2007.
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688:. London: Taylor & Francis Group. p.
278:probability of becoming included in the study.
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2181:Negative conclusion from affirmative premises
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1343:
1253:Böttcher L, D'Orsogna MR, Chou T (May 2021).
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165:likely to have another given disease such as
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1054:(4th ed.). Harcourt Brace Jovanovich.
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837:Ards S, Chung C, Myers SL (February 1998).
520:Statistical corrections for a biased sample
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2006:Heuristics in judgment and decision-making
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880:"Sample Selection Bias Correction Theory"
854:
211:
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713:RamĂrez i Soriano A (29 November 2008).
433:Another classic example occurred in the
387:
241:Simple pedigree example of sampling bias
236:
1020:
749:Society for Academic Emergency Medicine
735:
681:
670:Mosby's Medical Dictionary, 8th edition
504:, where variations in sampling bias in
3163:
1047:
731:
729:
500:A twenty-first century example is the
418:, would beat the incumbent president,
383:
233:Truncate selection in pedigree studies
3076:
2039:
1331:
993:
889:. Lecture Notes in Computer Science.
738:"Error and Bias in Clinical Research"
378:National Center for Health Statistics
316:
3120:Correlation does not imply causation
1229:
1171:Tancev G, Pascale C (October 2020).
1144:
1074:
16:Bias in the sampling of a population
726:
13:
1298:Journal of Social Service Research
325:peoples comes from caves, such as
14:
3192:
3139:Lies, damned lies, and statistics
1051:An Introduction to Human Genetics
3057:
3056:
1259:European Journal of Epidemiology
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466:contained the same names as the
74:, sometimes specifically termed
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1223:
1164:
1138:
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1103:
1068:
66:Distinction from selection bias
2554:Correlation implies causation
800:
782:Medilexicon Medical Dictionary
770:
706:
675:
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617:
1:
1240:10.13140/RG.2.2.24953.62564/1
1234:(Report). Bern, Switzerland.
856:10.1016/S0145-2134(97)00131-2
610:
341:Problems due to sampling bias
284:is a special case where each
249:trait. Following the laws of
121:human-subject research ethics
46:than others. It results in a
1151:The Engines of Our Ingenuity
1077:American Sociological Review
959:Theoretical Computer Science
7:
1872:DĂ©formation professionnelle
919:10.1007/978-3-540-87987-9_8
887:Algorithmic Learning Theory
537:
516:of cases across countries.
282:Complete truncate selection
177:, matching for an apparent
10:
3197:
3126:How to Lie with Statistics
2978:I'm entitled to my opinion
1866:Basking in reflected glory
1357:
1272:10.1007/s10654-021-00748-2
950:Cortes C, Mohri M (2014).
812:Dictionary of Cancer Terms
605:Truncated regression model
477:data, pollutants (such as
435:1948 presidential election
412:U.S. presidential election
396:) in use did not pass the
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2014:
1996:Cognitive bias mitigation
1988:
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1728:
1365:
1127:. Refsnes Data. June 2008
981:10.1016/j.tcs.2013.09.027
843:Child Abuse & Neglect
560:Censored regression model
437:. On election night, the
126:online and phone-in polls
3004:Motte-and-bailey fallacy
2104:Affirming the consequent
1580:Illusion of transparency
565:Cherry picking (fallacy)
97:
3024:Two wrongs make a right
2355:Denying the correlative
736:Panacek EA (May 2009).
493:) frequently show high
227:intellectual disability
42:have a lower or higher
3009:Psychologist's fallacy
2946:Argument to moderation
2936:Argument from anecdote
2886:Chronological snobbery
2510:Quoting out of context
2477:Overwhelming exception
2360:Suppressed correlative
2260:Quoting out of context
2135:quantificational logic
2109:Denying the antecedent
401:
242:
212:Symptom-based sampling
3171:Sampling (statistics)
3133:Impression management
2972:The Four Great Errors
2952:Argumentum ad populum
2941:Argument from silence
2645:Argumentum ad baculum
2423:Faulty generalization
2114:Argument from fallacy
1948:Arab–Israeli conflict
1675:Social influence bias
1620:Out-group homogeneity
1310:10.1300/J079v30n03_02
441:printed the headline
391:
265:Nontruncate selection
251:Mendelian inheritance
240:
76:sample selection bias
3176:Misuse of statistics
3104:Misuse of statistics
2990:Invincible ignorance
2796:Reductio ad Stalinum
2782:Reductio ad Hitlerum
2738:Wisdom of repugnance
2505:Moving the goalposts
2370:Illicit transference
2295:Begging the question
2216:Undistributed middle
2124:Mathematical fallacy
2099:Affirming a disjunct
1590:Mere-exposure effect
1520:Extrinsic incentives
1466:Selective perception
1125:"Browser Statistics"
778:"Ascertainment Bias"
585:Sampling probability
444:DEWEY DEFEATS TRUMAN
44:sampling probability
2723:Parade of horribles
2699:In-group favoritism
2525:Syntactic ambiguity
2168:Syllogistic fallacy
2091:propositional logic
1815:Social desirability
1710:von Restorff effect
1585:Mean world syndrome
1560:Hostile attribution
1189:2020Senso..20.6198T
747:. New Orleans, LA:
745:SAEM Annual Meeting
570:File drawer problem
510:case fatality rates
464:telephone directory
384:Historical examples
371:Demarcation Problem
2809:Poisoning the well
2626:Proof by assertion
2601:Texas sharpshooter
2535:Questionable cause
2472:Slothful induction
2431:Anecdotal evidence
2291:Circular reasoning
2186:Exclusive premises
2148:Illicit conversion
1730:Statistical biases
1508:Curse of knowledge
1048:Sutton HE (1988).
1021:Wallace R (2007).
997:Behavioral Science
682:Weising K (2005).
629:Medical Dictionary
595:Common source bias
575:Friendship paradox
552:Mathematics portal
531:attaching a weight
420:Franklin Roosevelt
402:
317:The caveman effect
271:Truncate selection
243:
105:specific real area
60:ascertainment bias
3181:Experimental bias
3158:
3157:
3115:Circular analysis
3070:
3069:
3048:
3047:
3044:
3043:
2984:Ignoratio elenchi
2896:
2895:
2746:
2745:
2708:Not invented here
2413:Converse accident
2335:Correlative-based
2312:Compound question
2255:False attribution
2250:False equivalence
2224:
2223:
2033:
2032:
1670:Social comparison
1451:Choice-supportive
1198:10.3390/s20216198
1111:"Minority Health"
1061:978-0-15-540099-3
1034:978-0-07-159318-2
1007:978-0-7817-8257-9
928:978-3-540-87986-2
758:on 17 August 2016
699:978-0-8493-1488-9
659:TheFreeDictionary
502:COVID-19 pandemic
483:nitrogen monoxide
394:Internet Explorer
273:. When afflicted
205:Spotlight fallacy
186:Survivorship bias
158:Berkson's fallacy
150:Healthy user bias
117:Non-response bias
103:Selection from a
88:internal validity
86:mainly addresses
80:external validity
3188:
3145:Misleading graph
3097:
3090:
3083:
3074:
3073:
3060:
3059:
3031:Special pleading
2910:
2909:
2771:Appeal to motive
2757:
2756:
2733:Stirring symbols
2713:Island mentality
2651:Wishful thinking
2632:
2631:
2348:Perfect solution
2325:No true Scotsman
2320:Complex question
2305:Leading question
2284:Question-begging
2270:No true Scotsman
2235:
2234:
2158:Quantifier shift
2153:Proof by example
2086:
2085:
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2046:
2037:
2036:
1830:Systematic error
1785:Omitted-variable
1700:Trait ascription
1540:Frog pond effect
1368:Cognitive biases
1352:
1345:
1338:
1329:
1328:
1322:
1321:
1293:
1287:
1286:
1284:
1274:
1250:
1244:
1243:
1227:
1221:
1220:
1210:
1200:
1168:
1162:
1161:
1159:
1157:
1142:
1136:
1135:
1133:
1132:
1121:
1115:
1114:
1107:
1101:
1100:
1072:
1066:
1065:
1045:
1039:
1038:
1018:
1012:
1011:
994:Fadem B (2009).
991:
985:
984:
974:
956:
947:
941:
940:
912:
902:
884:
875:
869:
868:
858:
834:
828:
827:
825:
823:
814:. Archived from
808:"Selection Bias"
804:
798:
797:
795:
793:
788:on 6 August 2016
784:. Archived from
774:
768:
767:
765:
763:
757:
751:. Archived from
742:
733:
724:
723:
721:
710:
704:
703:
679:
673:
672:
667:
665:
651:
645:
644:
642:
640:
635:on 10 March 2016
631:. Archived from
621:
554:
549:
548:
514:age distribution
506:COVID-19 testing
487:nitrogen dioxide
363:scientific fraud
312:
311:
307:
302:
301:
297:
247:simple Mendelian
21:Spotlight effect
3196:
3195:
3191:
3190:
3189:
3187:
3186:
3185:
3161:
3160:
3159:
3154:
3106:
3101:
3071:
3066:
3040:
3014:Rationalization
2957:
2904:
2892:
2830:
2752:Genetic fallacy
2742:
2655:
2630:
2605:
2529:
2520:Sorites paradox
2500:False precision
2481:
2462:Double counting
2417:
2392:
2364:
2329:
2316:Loaded question
2300:Loaded language
2279:
2220:
2162:
2128:
2075:
2064:
2034:
2029:
2010:
1984:
1849:
1724:
1705:Turkey illusion
1473:Compassion fade
1370:
1361:
1356:
1326:
1325:
1294:
1290:
1251:
1247:
1228:
1224:
1169:
1165:
1155:
1153:
1143:
1139:
1130:
1128:
1123:
1122:
1118:
1109:
1108:
1104:
1089:10.2307/2095230
1073:
1069:
1062:
1046:
1042:
1035:
1019:
1015:
1008:
992:
988:
972:10.1.1.367.6899
954:
948:
944:
929:
910:10.1.1.144.4478
882:
876:
872:
835:
831:
821:
819:
806:
805:
801:
791:
789:
776:
775:
771:
761:
759:
755:
740:
734:
727:
719:
711:
707:
700:
680:
676:
663:
661:
655:"Biased sample"
653:
652:
648:
638:
636:
625:"Sampling Bias"
623:
622:
618:
613:
550:
543:
540:
522:
479:carbon monoxide
468:Social Register
453:Harry S. Truman
449:president-elect
439:Chicago Tribune
407:Literary Digest
386:
343:
319:
309:
305:
304:
299:
295:
294:
235:
214:
115:bias (see also
100:
68:
24:
17:
12:
11:
5:
3194:
3184:
3183:
3178:
3173:
3156:
3155:
3153:
3152:
3147:
3142:
3135:
3130:
3122:
3117:
3111:
3108:
3107:
3100:
3099:
3092:
3085:
3077:
3068:
3067:
3065:
3064:
3053:
3050:
3049:
3046:
3045:
3042:
3041:
3039:
3038:
3033:
3028:
3027:
3026:
3016:
3011:
3006:
3001:
2992:
2987:
2980:
2975:
2968:
2962:
2959:
2958:
2956:
2955:
2948:
2943:
2938:
2933:
2932:
2931:
2918:
2916:
2907:
2898:
2897:
2894:
2893:
2891:
2890:
2889:
2888:
2874:
2869:
2864:
2863:
2862:
2853:
2846:
2844:Accomplishment
2835:
2832:
2831:
2829:
2828:
2823:
2816:
2811:
2806:
2801:
2800:
2799:
2792:
2791:
2790:
2773:
2767:
2765:
2754:
2748:
2747:
2744:
2743:
2741:
2740:
2735:
2730:
2725:
2720:
2715:
2710:
2701:
2696:
2691:
2686:
2681:
2676:
2671:
2665:
2663:
2657:
2656:
2654:
2653:
2648:
2640:
2638:
2629:
2628:
2619:
2613:
2611:
2607:
2606:
2604:
2603:
2598:
2596:Slippery slope
2593:
2588:
2583:
2582:
2581:
2571:
2570:
2569:
2562:
2552:
2551:
2550:
2539:
2537:
2531:
2530:
2528:
2527:
2522:
2517:
2515:Slippery slope
2512:
2507:
2502:
2497:
2491:
2489:
2483:
2482:
2480:
2479:
2474:
2469:
2464:
2459:
2450:
2449:
2448:
2443:
2441:Cherry picking
2433:
2427:
2425:
2419:
2418:
2416:
2415:
2410:
2404:
2402:
2394:
2393:
2391:
2390:
2385:
2380:
2374:
2372:
2366:
2365:
2363:
2362:
2357:
2352:
2351:
2350:
2339:
2337:
2331:
2330:
2328:
2327:
2322:
2309:
2308:
2307:
2297:
2287:
2285:
2281:
2280:
2278:
2277:
2272:
2267:
2262:
2257:
2252:
2247:
2241:
2239:
2232:
2226:
2225:
2222:
2221:
2219:
2218:
2213:
2208:
2203:
2198:
2193:
2188:
2183:
2178:
2172:
2170:
2164:
2163:
2161:
2160:
2155:
2150:
2145:
2139:
2137:
2130:
2129:
2127:
2126:
2121:
2116:
2111:
2106:
2101:
2095:
2093:
2083:
2077:
2076:
2063:
2062:
2055:
2048:
2040:
2031:
2030:
2028:
2027:
2022:
2015:
2012:
2011:
2009:
2008:
2003:
1998:
1992:
1990:
1989:Bias reduction
1986:
1985:
1983:
1982:
1977:
1972:
1967:
1965:Political bias
1962:
1957:
1956:
1955:
1950:
1945:
1940:
1935:
1930:
1925:
1920:
1910:
1905:
1900:
1895:
1893:Infrastructure
1890:
1885:
1880:
1875:
1868:
1863:
1857:
1855:
1851:
1850:
1848:
1847:
1842:
1837:
1832:
1827:
1822:
1817:
1812:
1810:Self-selection
1807:
1802:
1797:
1792:
1787:
1782:
1777:
1772:
1767:
1762:
1761:
1760:
1750:
1745:
1740:
1734:
1732:
1726:
1725:
1723:
1722:
1717:
1712:
1707:
1702:
1697:
1692:
1687:
1682:
1677:
1672:
1667:
1662:
1657:
1652:
1647:
1645:Pro-innovation
1642:
1637:
1632:
1630:Overton window
1627:
1622:
1617:
1612:
1607:
1602:
1597:
1592:
1587:
1582:
1577:
1572:
1567:
1562:
1557:
1552:
1547:
1542:
1537:
1532:
1527:
1522:
1517:
1512:
1511:
1510:
1500:
1498:Dunning–Kruger
1495:
1490:
1485:
1480:
1475:
1470:
1469:
1468:
1458:
1453:
1448:
1443:
1438:
1437:
1436:
1426:
1421:
1416:
1415:
1414:
1412:Correspondence
1409:
1407:Actor–observer
1399:
1394:
1389:
1384:
1379:
1373:
1371:
1366:
1363:
1362:
1355:
1354:
1347:
1340:
1332:
1324:
1323:
1288:
1265:(5): 545–558.
1245:
1222:
1163:
1137:
1116:
1102:
1083:(3): 386–398.
1067:
1060:
1040:
1033:
1013:
1006:
986:
942:
927:
870:
829:
818:on 9 June 2009
799:
769:
725:
705:
698:
674:
646:
615:
614:
612:
609:
608:
607:
602:
597:
592:
590:Selection bias
587:
582:
580:Reporting bias
577:
572:
567:
562:
556:
555:
539:
536:
521:
518:
385:
382:
342:
339:
327:cave paintings
318:
315:
290:
289:
279:
268:
234:
231:
213:
210:
209:
208:
199:
198:
194:Malmquist bias
190:
182:
170:
154:
146:
143:selection bias
130:
129:
112:Self-selection
108:
99:
96:
84:selection bias
72:selection bias
67:
64:
15:
9:
6:
4:
3:
2:
3193:
3182:
3179:
3177:
3174:
3172:
3169:
3168:
3166:
3151:
3150:Sampling bias
3148:
3146:
3143:
3140:
3136:
3134:
3131:
3128:
3127:
3123:
3121:
3118:
3116:
3113:
3112:
3109:
3105:
3098:
3093:
3091:
3086:
3084:
3079:
3078:
3075:
3063:
3055:
3054:
3051:
3037:
3034:
3032:
3029:
3025:
3022:
3021:
3020:
3017:
3015:
3012:
3010:
3007:
3005:
3002:
3000:
2996:
2993:
2991:
2988:
2986:
2985:
2981:
2979:
2976:
2974:
2973:
2969:
2967:
2964:
2963:
2960:
2954:
2953:
2949:
2947:
2944:
2942:
2939:
2937:
2934:
2930:
2927:
2926:
2925:
2924:
2920:
2919:
2917:
2915:
2911:
2908:
2906:
2899:
2887:
2884:
2883:
2882:
2878:
2875:
2873:
2870:
2868:
2865:
2861:
2857:
2854:
2852:
2851:
2847:
2845:
2842:
2841:
2840:
2837:
2836:
2833:
2827:
2824:
2822:
2821:
2817:
2815:
2812:
2810:
2807:
2805:
2802:
2798:
2797:
2793:
2789:
2786:
2785:
2784:
2783:
2779:
2778:
2777:
2774:
2772:
2769:
2768:
2766:
2764:
2763:
2758:
2755:
2753:
2749:
2739:
2736:
2734:
2731:
2729:
2726:
2724:
2721:
2719:
2716:
2714:
2711:
2709:
2705:
2704:Invented here
2702:
2700:
2697:
2695:
2692:
2690:
2687:
2685:
2682:
2680:
2677:
2675:
2672:
2670:
2667:
2666:
2664:
2662:
2658:
2652:
2649:
2647:
2646:
2642:
2641:
2639:
2637:
2633:
2627:
2623:
2620:
2618:
2615:
2614:
2612:
2608:
2602:
2599:
2597:
2594:
2592:
2589:
2587:
2584:
2580:
2577:
2576:
2575:
2572:
2568:
2567:
2563:
2561:
2560:
2556:
2555:
2553:
2549:
2546:
2545:
2544:
2541:
2540:
2538:
2536:
2532:
2526:
2523:
2521:
2518:
2516:
2513:
2511:
2508:
2506:
2503:
2501:
2498:
2496:
2493:
2492:
2490:
2488:
2484:
2478:
2475:
2473:
2470:
2468:
2467:False analogy
2465:
2463:
2460:
2458:
2454:
2451:
2447:
2444:
2442:
2439:
2438:
2437:
2436:Sampling bias
2434:
2432:
2429:
2428:
2426:
2424:
2420:
2414:
2411:
2409:
2406:
2405:
2403:
2401:
2400:
2399:Secundum quid
2395:
2389:
2386:
2384:
2381:
2379:
2376:
2375:
2373:
2371:
2367:
2361:
2358:
2356:
2353:
2349:
2346:
2345:
2344:
2343:False dilemma
2341:
2340:
2338:
2336:
2332:
2326:
2323:
2321:
2317:
2313:
2310:
2306:
2303:
2302:
2301:
2298:
2296:
2292:
2289:
2288:
2286:
2282:
2276:
2273:
2271:
2268:
2266:
2263:
2261:
2258:
2256:
2253:
2251:
2248:
2246:
2243:
2242:
2240:
2236:
2233:
2231:
2227:
2217:
2214:
2212:
2211:Illicit minor
2209:
2207:
2206:Illicit major
2204:
2202:
2199:
2197:
2194:
2192:
2189:
2187:
2184:
2182:
2179:
2177:
2174:
2173:
2171:
2169:
2165:
2159:
2156:
2154:
2151:
2149:
2146:
2144:
2141:
2140:
2138:
2136:
2131:
2125:
2122:
2120:
2117:
2115:
2112:
2110:
2107:
2105:
2102:
2100:
2097:
2096:
2094:
2092:
2087:
2084:
2082:
2078:
2073:
2069:
2061:
2056:
2054:
2049:
2047:
2042:
2041:
2038:
2026:
2023:
2021:
2017:
2016:
2013:
2007:
2004:
2002:
1999:
1997:
1994:
1993:
1991:
1987:
1981:
1978:
1976:
1973:
1971:
1968:
1966:
1963:
1961:
1958:
1954:
1951:
1949:
1946:
1944:
1943:United States
1941:
1939:
1936:
1934:
1931:
1929:
1926:
1924:
1921:
1919:
1918:False balance
1916:
1915:
1914:
1911:
1909:
1906:
1904:
1901:
1899:
1896:
1894:
1891:
1889:
1886:
1884:
1881:
1879:
1876:
1874:
1873:
1869:
1867:
1864:
1862:
1859:
1858:
1856:
1852:
1846:
1843:
1841:
1838:
1836:
1833:
1831:
1828:
1826:
1823:
1821:
1818:
1816:
1813:
1811:
1808:
1806:
1803:
1801:
1798:
1796:
1793:
1791:
1790:Participation
1788:
1786:
1783:
1781:
1778:
1776:
1773:
1771:
1768:
1766:
1763:
1759:
1758:Psychological
1756:
1755:
1754:
1751:
1749:
1746:
1744:
1741:
1739:
1736:
1735:
1733:
1731:
1727:
1721:
1718:
1716:
1713:
1711:
1708:
1706:
1703:
1701:
1698:
1696:
1693:
1691:
1688:
1686:
1683:
1681:
1678:
1676:
1673:
1671:
1668:
1666:
1663:
1661:
1658:
1656:
1653:
1651:
1648:
1646:
1643:
1641:
1638:
1636:
1633:
1631:
1628:
1626:
1623:
1621:
1618:
1616:
1613:
1611:
1608:
1606:
1603:
1601:
1598:
1596:
1593:
1591:
1588:
1586:
1583:
1581:
1578:
1576:
1573:
1571:
1568:
1566:
1563:
1561:
1558:
1556:
1553:
1551:
1548:
1546:
1543:
1541:
1538:
1536:
1533:
1531:
1528:
1526:
1525:Fading affect
1523:
1521:
1518:
1516:
1513:
1509:
1506:
1505:
1504:
1501:
1499:
1496:
1494:
1491:
1489:
1486:
1484:
1481:
1479:
1476:
1474:
1471:
1467:
1464:
1463:
1462:
1459:
1457:
1454:
1452:
1449:
1447:
1444:
1442:
1439:
1435:
1432:
1431:
1430:
1427:
1425:
1422:
1420:
1417:
1413:
1410:
1408:
1405:
1404:
1403:
1400:
1398:
1395:
1393:
1390:
1388:
1385:
1383:
1380:
1378:
1375:
1374:
1372:
1369:
1364:
1360:
1353:
1348:
1346:
1341:
1339:
1334:
1333:
1330:
1319:
1315:
1311:
1307:
1303:
1299:
1292:
1283:
1278:
1273:
1268:
1264:
1260:
1256:
1249:
1241:
1237:
1233:
1226:
1218:
1214:
1209:
1204:
1199:
1194:
1190:
1186:
1182:
1178:
1174:
1167:
1152:
1148:
1147:"Gallup Poll"
1145:Lienhard JH.
1141:
1126:
1120:
1112:
1106:
1098:
1094:
1090:
1086:
1082:
1078:
1071:
1063:
1057:
1053:
1052:
1044:
1036:
1030:
1026:
1025:
1017:
1009:
1003:
999:
998:
990:
982:
978:
973:
968:
964:
960:
953:
946:
938:
934:
930:
924:
920:
916:
911:
906:
901:
896:
892:
888:
881:
874:
866:
862:
857:
852:
849:(2): 103–15.
848:
844:
840:
833:
817:
813:
809:
803:
787:
783:
779:
773:
754:
750:
746:
739:
732:
730:
718:
717:
709:
701:
695:
691:
687:
686:
678:
671:
660:
656:
650:
634:
630:
626:
620:
616:
606:
603:
601:
600:Spectrum bias
598:
596:
593:
591:
588:
586:
583:
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2999:Naturalistic
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2905:of relevance
2848:
2826:Whataboutism
2818:
2794:
2788:Godwin's law
2780:
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2643:
2636:Consequences
2617:Law/Legality
2591:Single cause
2564:
2557:
2435:
2397:
2265:Loki's Wager
2245:Equivocation
2238:Equivocation
1903:In education
1870:
1854:Other biases
1840:Verification
1825:Survivorship
1799:
1775:Non-response
1748:Healthy user
1690:Substitution
1665:Self-serving
1461:Confirmation
1429:Availability
1377:Acquiescence
1304:(3): 19–33.
1301:
1297:
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1183:(21): 6198.
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1156:29 September
1154:. Retrieved
1150:
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1129:. Retrieved
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822:23 September
820:. Retrieved
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753:the original
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664:23 September
662:. Retrieved
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637:. Retrieved
633:the original
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457:phone survey
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3019:Red herring
2776:Association
2457:Conjunction
2378:Composition
2275:Reification
2191:Existential
2143:Existential
1970:Publication
1923:Vietnam War
1770:Length time
1753:Information
1695:Time-saving
1555:Horn effect
1545:Halo effect
1493:Distinction
1402:Attribution
1397:Attentional
965:: 103–126.
792:14 November
762:14 November
475:air quality
461:Bell System
428:Gallup poll
367:fold change
323:prehistoric
275:individuals
223:comorbidity
3165:Categories
2995:Moralistic
2929:Sealioning
2923:Ad nauseam
2850:Ipse dixit
2762:Ad hominem
2586:Regression
2388:Ecological
2201:Four terms
2119:Masked man
1933:South Asia
1908:Liking gap
1720:In animals
1685:Status quo
1600:Negativity
1503:Egocentric
1478:Congruence
1456:Commitment
1446:Blind spot
1434:Mean world
1424:Automation
1131:2008-07-05
611:References
416:Alf Landon
179:confounder
40:population
28:statistics
3036:Straw man
2914:Arguments
2903:fallacies
2877:Tradition
2867:Etymology
2839:Authority
2820:Tu quoque
2804:Bulverism
2574:Gambler's
2543:Animistic
2487:Ambiguity
2453:Base rate
2196:Necessity
2068:fallacies
2001:Debiasing
1980:White hat
1975:Reporting
1888:Inductive
1805:Selection
1765:Lead time
1738:Estimator
1715:Zero-risk
1680:Spotlight
1660:Restraint
1650:Proximity
1635:Precision
1595:Narrative
1550:Hindsight
1535:Frequency
1515:Emotional
1488:Declinism
1419:Authority
1392:Anchoring
1382:Ambiguity
967:CiteSeerX
905:CiteSeerX
900:0805.2775
893:: 38–53.
357:The word
351:parameter
347:statistic
134:Exclusion
3062:Category
2694:Ridicule
2679:Flattery
2669:Children
2566:Post hoc
2446:McNamara
2408:Accident
2383:Division
2230:Informal
1898:Inherent
1861:Academic
1835:Systemic
1820:Spectrum
1800:Sampling
1780:Observer
1743:Forecast
1655:Response
1615:Optimism
1610:Omission
1605:Normalcy
1575:In-group
1570:Implicit
1483:Cultural
1387:Affinity
1318:11685550
1232:Preprint
1217:33143233
538:See also
512:and the
219:dyslexia
138:migrated
53:sampling
2881:Novelty
2856:Poverty
2718:Loyalty
2684:Novelty
2661:Emotion
2610:Appeals
2579:Inverse
2559:Cum hoc
2548:Furtive
2066:Common
2020:General
2018:Lists:
1953:Ukraine
1878:Funding
1640:Present
1625:Outcome
1530:Framing
1282:8127858
1208:7662848
1185:Bibcode
1177:Sensors
1097:2095230
865:9504213
331:middens
308:⁄
298:⁄
3129:(1954)
2966:Cliché
2901:Other
2872:Nature
2860:Wealth
2495:Accent
2081:Formal
2025:Memory
1938:Sweden
1928:Norway
1795:Recall
1565:Impact
1441:Belief
1359:Biases
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937:842488
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286:family
2728:Spite
2622:Stone
1913:Media
1883:FUTON
1314:S2CID
1093:JSTOR
955:(PDF)
933:S2CID
895:arXiv
883:(PDF)
756:(PDF)
741:(PDF)
720:(PDF)
491:ozone
489:, or
398:Acid2
98:Types
34:is a
2814:Tone
2689:Pity
2674:Fear
2072:list
1213:PMID
1158:2007
1056:ISBN
1029:ISBN
1002:ISBN
923:ISBN
891:5254
861:PMID
824:2009
794:2009
764:2009
694:ISBN
666:2009
641:2009
359:bias
163:more
36:bias
2133:In
2089:In
1960:Net
1845:Wet
1306:doi
1277:PMC
1267:doi
1236:doi
1203:PMC
1193:doi
1085:doi
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963:519
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