118:. 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).
508:, 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.
557:
376:. 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.
380:) 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
139:, 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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391:, 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.
249:
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433:, 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
278:. 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.
535:
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
134:
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
268:). 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.
288:
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
470:. 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
164:, 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.
240:, 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.
61:
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.
200:, 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.
151:
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
348:, 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.
101:
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.
227:
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
172:, 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
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256:
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
340:
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.
763:
218:, 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.
232:
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.
1958:
415:
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
466:, 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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89:, but some classify it as a separate type of bias. A distinction, albeit not universally accepted, of sampling bias is that it undermines the
2191:
130:), which is possible whenever the group of people being studied has any form of control over whether to participate (as current standards of
1933:
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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
788:
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445:
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264:, 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
1963:
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481:). 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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73:. Ascertainment bias has basically the same definition, but is still sometimes classified as a separate type of bias.
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2380:
1307:
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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1953:
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208:, an effect in observational astronomy which leads to the preferential detection of intrinsically bright objects.
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1184:"The Relocation Problem of Field Calibrated Low-Cost Sensor Systems in Air Quality Monitoring: A Sampling Bias"
147:
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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850:"The effects of sample selection bias on racial differences in child abuse reporting"
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437:'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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963:"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
1266:"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.
38:
1208:
30:"Spotlight fallacy" redirects here. For the psychological effect, see
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2011:
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DNA fingerprinting in plants: principles, methods, and applications
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458:, 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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243:
1035:
Maxcy-Rosenau-Last Public Health and
Preventive Medicine
1038:(15th ed.). McGraw Hill Professional. p. 21.
519:
have been shown to account for wide variations in both
733:(Ph.D. thesis). Universitat Pompeu Fabra. p. 34.
552:
81:
Sampling bias is usually classified as a subtype of
69:
Medical sources sometimes refer to sampling bias as
889:Cortes C, Mohri M, Riley M, Rostamizadeh A (2008).
1011:. Lippincott Williams & Wilkins. p. 262.
76:
723:
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351:
954:
2187:Affirmative conclusion from a negative premise
1124:. National Center for Health Statistics. 2007.
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699:. London: Taylor & Francis Group. p.
289:probability of becoming included in the study.
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2192:Negative conclusion from affirmative premises
2062:
1354:
1264:Böttcher L, D'Orsogna MR, Chou T (May 2021).
1181:
176:likely to have another given disease such as
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1065:(4th ed.). Harcourt Brace Jovanovich.
1025:
960:
848:Ards S, Chung C, Myers SL (February 1998).
531:Statistical corrections for a biased sample
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2017:Heuristics in judgment and decision-making
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891:"Sample Selection Bias Correction Theory"
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222:
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724:RamĂrez i Soriano A (29 November 2008).
444:Another classic example occurred in the
398:
252:Simple pedigree example of sampling bias
247:
1031:
760:Society for Academic Emergency Medicine
746:
692:
681:Mosby's Medical Dictionary, 8th edition
515:, where variations in sampling bias in
14:
3174:
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742:
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511:A twenty-first century example is the
429:, would beat the incumbent president,
394:
244:Truncate selection in pedigree studies
3087:
2050:
1342:
1004:
900:. Lecture Notes in Computer Science.
749:"Error and Bias in Clinical Research"
389:National Center for Health Statistics
327:
3131:Correlation does not imply causation
1240:
1182:Tancev G, Pascale C (October 2020).
1155:
1085:
27:Bias in the sampling of a population
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24:
1309:Journal of Social Service Research
336:peoples comes from caves, such as
25:
3203:
3150:Lies, damned lies, and statistics
1062:An Introduction to Human Genetics
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3067:
1270:European Journal of Epidemiology
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477:contained the same names as the
85:, sometimes specifically termed
1300:
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1234:
1175:
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1114:
1079:
77:Distinction from selection bias
2565:Correlation implies causation
811:
793:Medilexicon Medical Dictionary
781:
717:
686:
658:
628:
13:
1:
1251:10.13140/RG.2.2.24953.62564/1
1245:(Report). Bern, Switzerland.
867:10.1016/S0145-2134(97)00131-2
621:
352:Problems due to sampling bias
295:is a special case where each
260:trait. Following the laws of
132:human-subject research ethics
57:than others. It results in a
1162:The Engines of Our Ingenuity
1088:American Sociological Review
970:Theoretical Computer Science
7:
1883:DĂ©formation professionnelle
930:10.1007/978-3-540-87987-9_8
898:Algorithmic Learning Theory
548:
527:of cases across countries.
293:Complete truncate selection
188:, matching for an apparent
10:
3208:
3137:How to Lie with Statistics
2989:I'm entitled to my opinion
1877:Basking in reflected glory
1368:
1283:10.1007/s10654-021-00748-2
961:Cortes C, Mohri M (2014).
823:Dictionary of Cancer Terms
616:Truncated regression model
488:data, pollutants (such as
446:1948 presidential election
423:U.S. presidential election
407:) in use did not pass the
29:
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2007:Cognitive bias mitigation
1999:
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1739:
1376:
1138:. Refsnes Data. June 2008
992:10.1016/j.tcs.2013.09.027
854:Child Abuse & Neglect
571:Censored regression model
448:. On election night, the
137:online and phone-in polls
3015:Motte-and-bailey fallacy
2115:Affirming the consequent
1591:Illusion of transparency
576:Cherry picking (fallacy)
108:
3035:Two wrongs make a right
2366:Denying the correlative
747:Panacek EA (May 2009).
504:) frequently show high
238:intellectual disability
53:have a lower or higher
3020:Psychologist's fallacy
2957:Argument to moderation
2947:Argument from anecdote
2897:Chronological snobbery
2521:Quoting out of context
2488:Overwhelming exception
2371:Suppressed correlative
2271:Quoting out of context
2146:quantificational logic
2120:Denying the antecedent
412:
253:
223:Symptom-based sampling
3182:Sampling (statistics)
3144:Impression management
2983:The Four Great Errors
2963:Argumentum ad populum
2952:Argument from silence
2656:Argumentum ad baculum
2434:Faulty generalization
2125:Argument from fallacy
1959:Arab–Israeli conflict
1686:Social influence bias
1631:Out-group homogeneity
1321:10.1300/J079v30n03_02
452:printed the headline
402:
276:Nontruncate selection
262:Mendelian inheritance
251:
87:sample selection bias
3187:Misuse of statistics
3115:Misuse of statistics
3001:Invincible ignorance
2807:Reductio ad Stalinum
2793:Reductio ad Hitlerum
2749:Wisdom of repugnance
2516:Moving the goalposts
2381:Illicit transference
2306:Begging the question
2227:Undistributed middle
2135:Mathematical fallacy
2110:Affirming a disjunct
1601:Mere-exposure effect
1531:Extrinsic incentives
1477:Selective perception
1136:"Browser Statistics"
789:"Ascertainment Bias"
596:Sampling probability
455:DEWEY DEFEATS TRUMAN
55:sampling probability
2734:Parade of horribles
2710:In-group favoritism
2536:Syntactic ambiguity
2179:Syllogistic fallacy
2102:propositional logic
1826:Social desirability
1721:von Restorff effect
1596:Mean world syndrome
1571:Hostile attribution
1200:2020Senso..20.6198T
758:. New Orleans, LA:
756:SAEM Annual Meeting
581:File drawer problem
521:case fatality rates
475:telephone directory
395:Historical examples
382:Demarcation Problem
2820:Poisoning the well
2637:Proof by assertion
2612:Texas sharpshooter
2546:Questionable cause
2483:Slothful induction
2442:Anecdotal evidence
2302:Circular reasoning
2197:Exclusive premises
2159:Illicit conversion
1741:Statistical biases
1519:Curse of knowledge
1059:Sutton HE (1988).
1032:Wallace R (2007).
1008:Behavioral Science
693:Weising K (2005).
640:Medical Dictionary
606:Common source bias
586:Friendship paradox
563:Mathematics portal
542:attaching a weight
431:Franklin Roosevelt
413:
328:The caveman effect
282:Truncate selection
254:
116:specific real area
71:ascertainment bias
3192:Experimental bias
3169:
3168:
3126:Circular analysis
3081:
3080:
3059:
3058:
3055:
3054:
2995:Ignoratio elenchi
2907:
2906:
2757:
2756:
2719:Not invented here
2424:Converse accident
2346:Correlative-based
2323:Compound question
2266:False attribution
2261:False equivalence
2235:
2234:
2044:
2043:
1681:Social comparison
1462:Choice-supportive
1209:10.3390/s20216198
1122:"Minority Health"
1072:978-0-15-540099-3
1045:978-0-07-159318-2
1018:978-0-7817-8257-9
939:978-3-540-87986-2
769:on 17 August 2016
710:978-0-8493-1488-9
670:TheFreeDictionary
513:COVID-19 pandemic
494:nitrogen monoxide
405:Internet Explorer
284:. When afflicted
216:Spotlight fallacy
197:Survivorship bias
169:Berkson's fallacy
161:Healthy user bias
128:Non-response bias
114:Selection from a
99:internal validity
97:mainly addresses
91:external validity
16:(Redirected from
3199:
3156:Misleading graph
3108:
3101:
3094:
3085:
3084:
3071:
3070:
3042:Special pleading
2921:
2920:
2782:Appeal to motive
2768:
2767:
2744:Stirring symbols
2724:Island mentality
2662:Wishful thinking
2643:
2642:
2359:Perfect solution
2336:No true Scotsman
2331:Complex question
2316:Leading question
2295:Question-begging
2281:No true Scotsman
2246:
2245:
2169:Quantifier shift
2164:Proof by example
2097:
2096:
2071:
2064:
2057:
2048:
2047:
1841:Systematic error
1796:Omitted-variable
1711:Trait ascription
1551:Frog pond effect
1379:Cognitive biases
1363:
1356:
1349:
1340:
1339:
1333:
1332:
1304:
1298:
1297:
1295:
1285:
1261:
1255:
1254:
1238:
1232:
1231:
1221:
1211:
1179:
1173:
1172:
1170:
1168:
1153:
1147:
1146:
1144:
1143:
1132:
1126:
1125:
1118:
1112:
1111:
1083:
1077:
1076:
1056:
1050:
1049:
1029:
1023:
1022:
1005:Fadem B (2009).
1002:
996:
995:
985:
967:
958:
952:
951:
923:
913:
895:
886:
880:
879:
869:
845:
839:
838:
836:
834:
825:. Archived from
819:"Selection Bias"
815:
809:
808:
806:
804:
799:on 6 August 2016
795:. Archived from
785:
779:
778:
776:
774:
768:
762:. Archived from
753:
744:
735:
734:
732:
721:
715:
714:
690:
684:
683:
678:
676:
662:
656:
655:
653:
651:
646:on 10 March 2016
642:. Archived from
632:
565:
560:
559:
525:age distribution
517:COVID-19 testing
498:nitrogen dioxide
374:scientific fraud
323:
322:
318:
313:
312:
308:
258:simple Mendelian
32:Spotlight effect
21:
3207:
3206:
3202:
3201:
3200:
3198:
3197:
3196:
3172:
3171:
3170:
3165:
3117:
3112:
3082:
3077:
3051:
3025:Rationalization
2968:
2915:
2903:
2841:
2763:Genetic fallacy
2753:
2666:
2641:
2616:
2540:
2531:Sorites paradox
2511:False precision
2492:
2473:Double counting
2428:
2403:
2375:
2340:
2327:Loaded question
2311:Loaded language
2290:
2231:
2173:
2139:
2086:
2075:
2045:
2040:
2021:
1995:
1860:
1735:
1716:Turkey illusion
1484:Compassion fade
1381:
1372:
1367:
1337:
1336:
1305:
1301:
1262:
1258:
1239:
1235:
1180:
1176:
1166:
1164:
1154:
1150:
1141:
1139:
1134:
1133:
1129:
1120:
1119:
1115:
1100:10.2307/2095230
1084:
1080:
1073:
1057:
1053:
1046:
1030:
1026:
1019:
1003:
999:
983:10.1.1.367.6899
965:
959:
955:
940:
921:10.1.1.144.4478
893:
887:
883:
846:
842:
832:
830:
817:
816:
812:
802:
800:
787:
786:
782:
772:
770:
766:
751:
745:
738:
730:
722:
718:
711:
691:
687:
674:
672:
666:"Biased sample"
664:
663:
659:
649:
647:
636:"Sampling Bias"
634:
633:
629:
624:
561:
554:
551:
533:
490:carbon monoxide
479:Social Register
464:Harry S. Truman
460:president-elect
450:Chicago Tribune
418:Literary Digest
397:
354:
330:
320:
316:
315:
310:
306:
305:
246:
225:
126:bias (see also
111:
79:
35:
28:
23:
22:
15:
12:
11:
5:
3205:
3195:
3194:
3189:
3184:
3167:
3166:
3164:
3163:
3158:
3153:
3146:
3141:
3133:
3128:
3122:
3119:
3118:
3111:
3110:
3103:
3096:
3088:
3079:
3078:
3076:
3075:
3064:
3061:
3060:
3057:
3056:
3053:
3052:
3050:
3049:
3044:
3039:
3038:
3037:
3027:
3022:
3017:
3012:
3003:
2998:
2991:
2986:
2979:
2973:
2970:
2969:
2967:
2966:
2959:
2954:
2949:
2944:
2943:
2942:
2929:
2927:
2918:
2909:
2908:
2905:
2904:
2902:
2901:
2900:
2899:
2885:
2880:
2875:
2874:
2873:
2864:
2857:
2855:Accomplishment
2846:
2843:
2842:
2840:
2839:
2834:
2827:
2822:
2817:
2812:
2811:
2810:
2803:
2802:
2801:
2784:
2778:
2776:
2765:
2759:
2758:
2755:
2754:
2752:
2751:
2746:
2741:
2736:
2731:
2726:
2721:
2712:
2707:
2702:
2697:
2692:
2687:
2682:
2676:
2674:
2668:
2667:
2665:
2664:
2659:
2651:
2649:
2640:
2639:
2630:
2624:
2622:
2618:
2617:
2615:
2614:
2609:
2607:Slippery slope
2604:
2599:
2594:
2593:
2592:
2582:
2581:
2580:
2573:
2563:
2562:
2561:
2550:
2548:
2542:
2541:
2539:
2538:
2533:
2528:
2526:Slippery slope
2523:
2518:
2513:
2508:
2502:
2500:
2494:
2493:
2491:
2490:
2485:
2480:
2475:
2470:
2461:
2460:
2459:
2454:
2452:Cherry picking
2444:
2438:
2436:
2430:
2429:
2427:
2426:
2421:
2415:
2413:
2405:
2404:
2402:
2401:
2396:
2391:
2385:
2383:
2377:
2376:
2374:
2373:
2368:
2363:
2362:
2361:
2350:
2348:
2342:
2341:
2339:
2338:
2333:
2320:
2319:
2318:
2308:
2298:
2296:
2292:
2291:
2289:
2288:
2283:
2278:
2273:
2268:
2263:
2258:
2252:
2250:
2243:
2237:
2236:
2233:
2232:
2230:
2229:
2224:
2219:
2214:
2209:
2204:
2199:
2194:
2189:
2183:
2181:
2175:
2174:
2172:
2171:
2166:
2161:
2156:
2150:
2148:
2141:
2140:
2138:
2137:
2132:
2127:
2122:
2117:
2112:
2106:
2104:
2094:
2088:
2087:
2074:
2073:
2066:
2059:
2051:
2042:
2041:
2039:
2038:
2033:
2026:
2023:
2022:
2020:
2019:
2014:
2009:
2003:
2001:
2000:Bias reduction
1997:
1996:
1994:
1993:
1988:
1983:
1978:
1976:Political bias
1973:
1968:
1967:
1966:
1961:
1956:
1951:
1946:
1941:
1936:
1931:
1921:
1916:
1911:
1906:
1904:Infrastructure
1901:
1896:
1891:
1886:
1879:
1874:
1868:
1866:
1862:
1861:
1859:
1858:
1853:
1848:
1843:
1838:
1833:
1828:
1823:
1821:Self-selection
1818:
1813:
1808:
1803:
1798:
1793:
1788:
1783:
1778:
1773:
1772:
1771:
1761:
1756:
1751:
1745:
1743:
1737:
1736:
1734:
1733:
1728:
1723:
1718:
1713:
1708:
1703:
1698:
1693:
1688:
1683:
1678:
1673:
1668:
1663:
1658:
1656:Pro-innovation
1653:
1648:
1643:
1641:Overton window
1638:
1633:
1628:
1623:
1618:
1613:
1608:
1603:
1598:
1593:
1588:
1583:
1578:
1573:
1568:
1563:
1558:
1553:
1548:
1543:
1538:
1533:
1528:
1523:
1522:
1521:
1511:
1509:Dunning–Kruger
1506:
1501:
1496:
1491:
1486:
1481:
1480:
1479:
1469:
1464:
1459:
1454:
1449:
1448:
1447:
1437:
1432:
1427:
1426:
1425:
1423:Correspondence
1420:
1418:Actor–observer
1410:
1405:
1400:
1395:
1390:
1384:
1382:
1377:
1374:
1373:
1366:
1365:
1358:
1351:
1343:
1335:
1334:
1299:
1276:(5): 545–558.
1256:
1233:
1174:
1148:
1127:
1113:
1094:(3): 386–398.
1078:
1071:
1051:
1044:
1024:
1017:
997:
953:
938:
881:
840:
829:on 9 June 2009
810:
780:
736:
716:
709:
685:
657:
626:
625:
623:
620:
619:
618:
613:
608:
603:
601:Selection bias
598:
593:
591:Reporting bias
588:
583:
578:
573:
567:
566:
550:
547:
532:
529:
396:
393:
353:
350:
338:cave paintings
329:
326:
301:
300:
290:
279:
245:
242:
224:
221:
220:
219:
210:
209:
205:Malmquist bias
201:
193:
181:
165:
157:
154:selection bias
141:
140:
123:Self-selection
119:
110:
107:
95:selection bias
83:selection bias
78:
75:
26:
9:
6:
4:
3:
2:
3204:
3193:
3190:
3188:
3185:
3183:
3180:
3179:
3177:
3162:
3161:Sampling bias
3159:
3157:
3154:
3151:
3147:
3145:
3142:
3139:
3138:
3134:
3132:
3129:
3127:
3124:
3123:
3120:
3116:
3109:
3104:
3102:
3097:
3095:
3090:
3089:
3086:
3074:
3066:
3065:
3062:
3048:
3045:
3043:
3040:
3036:
3033:
3032:
3031:
3028:
3026:
3023:
3021:
3018:
3016:
3013:
3011:
3007:
3004:
3002:
2999:
2997:
2996:
2992:
2990:
2987:
2985:
2984:
2980:
2978:
2975:
2974:
2971:
2965:
2964:
2960:
2958:
2955:
2953:
2950:
2948:
2945:
2941:
2938:
2937:
2936:
2935:
2931:
2930:
2928:
2926:
2922:
2919:
2917:
2910:
2898:
2895:
2894:
2893:
2889:
2886:
2884:
2881:
2879:
2876:
2872:
2868:
2865:
2863:
2862:
2858:
2856:
2853:
2852:
2851:
2848:
2847:
2844:
2838:
2835:
2833:
2832:
2828:
2826:
2823:
2821:
2818:
2816:
2813:
2809:
2808:
2804:
2800:
2797:
2796:
2795:
2794:
2790:
2789:
2788:
2785:
2783:
2780:
2779:
2777:
2775:
2774:
2769:
2766:
2764:
2760:
2750:
2747:
2745:
2742:
2740:
2737:
2735:
2732:
2730:
2727:
2725:
2722:
2720:
2716:
2715:Invented here
2713:
2711:
2708:
2706:
2703:
2701:
2698:
2696:
2693:
2691:
2688:
2686:
2683:
2681:
2678:
2677:
2675:
2673:
2669:
2663:
2660:
2658:
2657:
2653:
2652:
2650:
2648:
2644:
2638:
2634:
2631:
2629:
2626:
2625:
2623:
2619:
2613:
2610:
2608:
2605:
2603:
2600:
2598:
2595:
2591:
2588:
2587:
2586:
2583:
2579:
2578:
2574:
2572:
2571:
2567:
2566:
2564:
2560:
2557:
2556:
2555:
2552:
2551:
2549:
2547:
2543:
2537:
2534:
2532:
2529:
2527:
2524:
2522:
2519:
2517:
2514:
2512:
2509:
2507:
2504:
2503:
2501:
2499:
2495:
2489:
2486:
2484:
2481:
2479:
2478:False analogy
2476:
2474:
2471:
2469:
2465:
2462:
2458:
2455:
2453:
2450:
2449:
2448:
2447:Sampling bias
2445:
2443:
2440:
2439:
2437:
2435:
2431:
2425:
2422:
2420:
2417:
2416:
2414:
2412:
2411:
2410:Secundum quid
2406:
2400:
2397:
2395:
2392:
2390:
2387:
2386:
2384:
2382:
2378:
2372:
2369:
2367:
2364:
2360:
2357:
2356:
2355:
2354:False dilemma
2352:
2351:
2349:
2347:
2343:
2337:
2334:
2332:
2328:
2324:
2321:
2317:
2314:
2313:
2312:
2309:
2307:
2303:
2300:
2299:
2297:
2293:
2287:
2284:
2282:
2279:
2277:
2274:
2272:
2269:
2267:
2264:
2262:
2259:
2257:
2254:
2253:
2251:
2247:
2244:
2242:
2238:
2228:
2225:
2223:
2222:Illicit minor
2220:
2218:
2217:Illicit major
2215:
2213:
2210:
2208:
2205:
2203:
2200:
2198:
2195:
2193:
2190:
2188:
2185:
2184:
2182:
2180:
2176:
2170:
2167:
2165:
2162:
2160:
2157:
2155:
2152:
2151:
2149:
2147:
2142:
2136:
2133:
2131:
2128:
2126:
2123:
2121:
2118:
2116:
2113:
2111:
2108:
2107:
2105:
2103:
2098:
2095:
2093:
2089:
2084:
2080:
2072:
2067:
2065:
2060:
2058:
2053:
2052:
2049:
2037:
2034:
2032:
2028:
2027:
2024:
2018:
2015:
2013:
2010:
2008:
2005:
2004:
2002:
1998:
1992:
1989:
1987:
1984:
1982:
1979:
1977:
1974:
1972:
1969:
1965:
1962:
1960:
1957:
1955:
1954:United States
1952:
1950:
1947:
1945:
1942:
1940:
1937:
1935:
1932:
1930:
1929:False balance
1927:
1926:
1925:
1922:
1920:
1917:
1915:
1912:
1910:
1907:
1905:
1902:
1900:
1897:
1895:
1892:
1890:
1887:
1885:
1884:
1880:
1878:
1875:
1873:
1870:
1869:
1867:
1863:
1857:
1854:
1852:
1849:
1847:
1844:
1842:
1839:
1837:
1834:
1832:
1829:
1827:
1824:
1822:
1819:
1817:
1814:
1812:
1809:
1807:
1804:
1802:
1801:Participation
1799:
1797:
1794:
1792:
1789:
1787:
1784:
1782:
1779:
1777:
1774:
1770:
1769:Psychological
1767:
1766:
1765:
1762:
1760:
1757:
1755:
1752:
1750:
1747:
1746:
1744:
1742:
1738:
1732:
1729:
1727:
1724:
1722:
1719:
1717:
1714:
1712:
1709:
1707:
1704:
1702:
1699:
1697:
1694:
1692:
1689:
1687:
1684:
1682:
1679:
1677:
1674:
1672:
1669:
1667:
1664:
1662:
1659:
1657:
1654:
1652:
1649:
1647:
1644:
1642:
1639:
1637:
1634:
1632:
1629:
1627:
1624:
1622:
1619:
1617:
1614:
1612:
1609:
1607:
1604:
1602:
1599:
1597:
1594:
1592:
1589:
1587:
1584:
1582:
1579:
1577:
1574:
1572:
1569:
1567:
1564:
1562:
1559:
1557:
1554:
1552:
1549:
1547:
1544:
1542:
1539:
1537:
1536:Fading affect
1534:
1532:
1529:
1527:
1524:
1520:
1517:
1516:
1515:
1512:
1510:
1507:
1505:
1502:
1500:
1497:
1495:
1492:
1490:
1487:
1485:
1482:
1478:
1475:
1474:
1473:
1470:
1468:
1465:
1463:
1460:
1458:
1455:
1453:
1450:
1446:
1443:
1442:
1441:
1438:
1436:
1433:
1431:
1428:
1424:
1421:
1419:
1416:
1415:
1414:
1411:
1409:
1406:
1404:
1401:
1399:
1396:
1394:
1391:
1389:
1386:
1385:
1383:
1380:
1375:
1371:
1364:
1359:
1357:
1352:
1350:
1345:
1344:
1341:
1330:
1326:
1322:
1318:
1314:
1310:
1303:
1294:
1289:
1284:
1279:
1275:
1271:
1267:
1260:
1252:
1248:
1244:
1237:
1229:
1225:
1220:
1215:
1210:
1205:
1201:
1197:
1193:
1189:
1185:
1178:
1163:
1159:
1158:"Gallup Poll"
1156:Lienhard JH.
1152:
1137:
1131:
1123:
1117:
1109:
1105:
1101:
1097:
1093:
1089:
1082:
1074:
1068:
1064:
1063:
1055:
1047:
1041:
1037:
1036:
1028:
1020:
1014:
1010:
1009:
1001:
993:
989:
984:
979:
975:
971:
964:
957:
949:
945:
941:
935:
931:
927:
922:
917:
912:
907:
903:
899:
892:
885:
877:
873:
868:
863:
860:(2): 103–15.
859:
855:
851:
844:
828:
824:
820:
814:
798:
794:
790:
784:
765:
761:
757:
750:
743:
741:
729:
728:
720:
712:
706:
702:
698:
697:
689:
682:
671:
667:
661:
645:
641:
637:
631:
627:
617:
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18:Biased sample
3160:
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3010:Naturalistic
2993:
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2916:of relevance
2859:
2837:Whataboutism
2829:
2805:
2799:Godwin's law
2791:
2771:
2654:
2647:Consequences
2628:Law/Legality
2602:Single cause
2575:
2568:
2446:
2408:
2276:Loki's Wager
2256:Equivocation
2249:Equivocation
1914:In education
1881:
1865:Other biases
1851:Verification
1836:Survivorship
1810:
1786:Non-response
1759:Healthy user
1701:Substitution
1676:Self-serving
1472:Confirmation
1440:Availability
1388:Acquiescence
1315:(3): 19–33.
1312:
1308:
1302:
1273:
1269:
1259:
1242:
1236:
1194:(21): 6198.
1191:
1187:
1177:
1167:29 September
1165:. Retrieved
1161:
1151:
1140:. Retrieved
1130:
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833:23 September
831:. Retrieved
827:the original
822:
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801:. Retrieved
797:the original
792:
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771:. Retrieved
764:the original
755:
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695:
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675:23 September
673:. Retrieved
669:
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650:23 September
648:. Retrieved
644:the original
639:
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510:
506:correlations
483:
468:phone survey
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346:burial sites
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3030:Red herring
2787:Association
2468:Conjunction
2389:Composition
2286:Reification
2202:Existential
2154:Existential
1981:Publication
1934:Vietnam War
1781:Length time
1764:Information
1706:Time-saving
1566:Horn effect
1556:Halo effect
1504:Distinction
1413:Attribution
1408:Attentional
976:: 103–126.
803:14 November
773:14 November
486:air quality
472:Bell System
439:Gallup poll
378:fold change
334:prehistoric
286:individuals
234:comorbidity
3176:Categories
3006:Moralistic
2940:Sealioning
2934:Ad nauseam
2861:Ipse dixit
2773:Ad hominem
2597:Regression
2399:Ecological
2212:Four terms
2130:Masked man
1944:South Asia
1919:Liking gap
1731:In animals
1696:Status quo
1611:Negativity
1514:Egocentric
1489:Congruence
1467:Commitment
1457:Blind spot
1445:Mean world
1435:Automation
1142:2008-07-05
622:References
427:Alf Landon
190:confounder
51:population
39:statistics
3047:Straw man
2925:Arguments
2914:fallacies
2888:Tradition
2878:Etymology
2850:Authority
2831:Tu quoque
2815:Bulverism
2585:Gambler's
2554:Animistic
2498:Ambiguity
2464:Base rate
2207:Necessity
2079:fallacies
2012:Debiasing
1991:White hat
1986:Reporting
1899:Inductive
1816:Selection
1776:Lead time
1749:Estimator
1726:Zero-risk
1691:Spotlight
1671:Restraint
1661:Proximity
1646:Precision
1606:Narrative
1561:Hindsight
1546:Frequency
1526:Emotional
1499:Declinism
1430:Authority
1403:Anchoring
1393:Ambiguity
978:CiteSeerX
916:CiteSeerX
911:0805.2775
904:: 38–53.
368:The word
362:parameter
358:statistic
145:Exclusion
3073:Category
2705:Ridicule
2690:Flattery
2680:Children
2577:Post hoc
2457:McNamara
2419:Accident
2394:Division
2241:Informal
1909:Inherent
1872:Academic
1846:Systemic
1831:Spectrum
1811:Sampling
1791:Observer
1754:Forecast
1666:Response
1626:Optimism
1621:Omission
1616:Normalcy
1586:In-group
1581:Implicit
1494:Cultural
1398:Affinity
1329:11685550
1243:Preprint
1228:33143233
549:See also
523:and the
230:dyslexia
149:migrated
64:sampling
2892:Novelty
2867:Poverty
2729:Loyalty
2695:Novelty
2672:Emotion
2621:Appeals
2590:Inverse
2570:Cum hoc
2559:Furtive
2077:Common
2031:General
2029:Lists:
1964:Ukraine
1889:Funding
1651:Present
1636:Outcome
1541:Framing
1293:8127858
1219:7662848
1196:Bibcode
1188:Sensors
1108:2095230
876:9504213
342:middens
319:⁄
309:⁄
3140:(1954)
2977:Cliché
2912:Other
2883:Nature
2871:Wealth
2506:Accent
2092:Formal
2036:Memory
1949:Sweden
1939:Norway
1806:Recall
1576:Impact
1452:Belief
1370:Biases
1327:
1290:
1226:
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1015:
980:
948:842488
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297:family
2739:Spite
2633:Stone
1924:Media
1894:FUTON
1325:S2CID
1104:JSTOR
966:(PDF)
944:S2CID
906:arXiv
894:(PDF)
767:(PDF)
752:(PDF)
731:(PDF)
502:ozone
500:, or
409:Acid2
109:Types
45:is a
2825:Tone
2700:Pity
2685:Fear
2083:list
1224:PMID
1169:2007
1067:ISBN
1040:ISBN
1013:ISBN
934:ISBN
902:5254
872:PMID
835:2009
805:2009
775:2009
705:ISBN
677:2009
652:2009
370:bias
174:more
47:bias
2144:In
2100:In
1971:Net
1856:Wet
1317:doi
1288:PMC
1278:doi
1247:doi
1214:PMC
1204:doi
1096:doi
988:doi
974:519
926:doi
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