Information bias in health research: definition, pitfalls, and adjustment methods — TEI-tagged view

Alaa Althubaiti · 2016 · epidemiology · doi:10.2147/jmdh.s104807 · TEI xml · tagged edition · index · concepts · authors
Where this record's text came from. 2026-08-23 schema-0.3 pass: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node.
This record’s own method is review, and the tags below follow from that. A review’s findings, quantities and statistics are things stated IN this paper, and some of them report results obtained elsewhere. No tag on this page names a different author. Measured across every review in this corpus, 81% of a review’s tags carry no citation at all — most of them are the review speaking. The corpus does not adjudicate the rest: read an unmarked tag as a claim this paper makes, not necessarily one it made first.
Other fields define these differently the same word, defined another way in another discipline
Bias 38 fields · confirmation bias 4 fields · random error 3 fields · measurement error 2 fields
Tags on this paper every tag the record carries, whether or not the words occur in the text below — a number is how many papers in the corpus carry that tag; a tag with no number is unique to this paper
Bias19 any systematic error in the design, conduct, or analysis of a study selection bias5 information bias measurement error5 measurement bias confirmation bias5 observer bias random sampling2 measurement error variability An analysis that ignores the effect of measurement error on the… a naïve analysis cases healthy controls The size of the difference between the observed rates of exposure to… the observed odds ratio review319
Method, as described: a narrative review of the bias literature, organised by type of information bias and by the study design in which each is most likely

Our tag layer for this paper

Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenonsystematic error introduced by how a measurement is obtained rather than by whom it is obtained from - and which is often introduced unintentionally, so that it is hard to recognise even by the researcher who caused it— “While a number of studies have discussed different types of bias, 2 – 4 the problem of bias is still frequently ignored in practice. Often bias is unintentionally introduced into a study by researchers, making it difficult to recognize, but it can also be introduced intentionally. Thus, bias remains a very sensitive issue to address and…
claim-typemethod— “Bias can be defined as any systematic error in the design, conduct, or analysis of a study. In health studies, bias can arise from two different sources; the approach adopted for selecting subjects for a study or the approach adopted for collecting or measuring data from a study. These are, respectively, termed as selection bias and…
method — evidence— “The aim of this paper is to raise the awareness of three specific forms of information bias in observational and experimental medical research study designs. These are self-reporting bias, and the often-marginalized measurement error bias, and confirmation bias. We present clear and simple strategies to improve the decision-making process. As…
object of study — evidence— “If these errors occur, observed measurements will differ from the actual values, and this is often referred to as measurement error, instrumental error, measurement imprecision, or measurement bias.”
research question — evidence— “The aim of this paper is to raise the awareness of three specific forms of information bias in observational and experimental medical research study designs. These are self-reporting bias, and the often-marginalized measurement error bias, and confirmation bias. We present clear and simple strategies to improve the decision-making process. As…
contribution — evidence— “The aim of this paper is to raise the awareness of three specific forms of information bias in observational and experimental medical research study designs. These are self-reporting bias, and the often-marginalized measurement error bias, and confirmation bias. We present clear and simple strategies to improve the decision-making process. As…
mechanismONE ERROR, FOUR NAMES, IN ONE SENTENCE: `If these errors occur, observed measurements will differ from the actual values, and this is often referred to as measurement error, instrumental error, measurement imprecision, or measurement bias.` Four names offered as interchangeable, with no test given for telling them apart - and one of them, `measurement bias`, names the consequence while the others name the cause— “If these errors occur, observed measurements will differ from the actual values, and this is often referred to as measurement error, instrumental error, measurement imprecision, or measurement bias.”mechanisms[0]
mechanismAND AGAIN FOR THE THIRD FORM: `Placing emphasis on one hypothesis because it does not contradict investigator beliefs is called confirmation bias, otherwise known as confirmatory, ascertainment, or observer bias.` And the paper's subject itself: `Information bias, otherwise known as misclassification`. Three concepts, four, four and two names, in one short review - the corpus holds papers that count 138 definitions of a term; this is the same phenomenon at the scale of a synonym list, and it is not remarked on— “Placing emphasis on one hypothesis because it does not contradict investigator beliefs is called confirmation bias, otherwise known as confirmatory, ascertainment, or observer bias. Confirmation bias is a type of psychological bias in which a decision is made according to the subject’s preconceptions, beliefs, or preferences. Such bias…mechanisms[1]
mechanismAND THE TOP-LEVEL DEFINITION IS A GENUS AND A DIFFERENTIA: `Bias can be defined as any systematic error in the design, conduct, or analysis of a study. In health studies, bias can arise from two different sources; the approach adopted for selecting subjects ... or the approach adopted for collecting or measuring data ... termed as selection bias and information bias.` The partition is by WHERE THE ERROR ENTERS, not by what it does to the estimate - which is why the same arithmetic consequence can belong to either— “Bias can be defined as any systematic error in the design, conduct, or…mechanisms[2]
mechanismAND A DISTINCTION THE FIELD IS EXPLICITLY WARNED NOT TO COLLAPSE: `However, random sampling should not be confused with measurement error variability. Commonly used statistical methods can address the sampling variability during data analysis, but they do not account for uncertainty due to measurement error.` The warning exists because the two are routinely conflated, and the cost of conflating them is that the standard toolbox appears to cover both and covers one
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mechanisms[3]
mechanismAND A TERM DEFINED BY WHAT IT IGNORES: `An analysis that ignores the effect of measurement error on the results can be referred to as a naive analysis.` Its consequences are given as `inconsistent (or biased) and/or inefficient estimators of regression parameters`. A name for the default practice, which is the rhetorical move that makes an omission visible
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mechanisms[4]
mechanismAND THE MECHANISM OF RECALL BIAS IS SPELLED OUT AS A CHAIN: `in a case-control study, cases are often more likely to recall exposure to risk factors than healthy controls. As such, true exposure might be underreported in healthy controls and overreported in the cases. The size of the difference ... will consequently be inflated, and, in turn, the observed odds ratio would also increase.` The bias has a direction, and the direction is predictable from the design - which is what makes it adjustable at all
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mechanisms[5]
mechanismAND THE FIX FOR SELF-REPORTING IS VALIDATION AGAINST SOMETHING ELSE: `internal validation` compares responses `with other data collection methods, such as laboratory measurements`; `external validation` uses `medical record checks or reports from family or friends`. The instrument is checked against a second instrument, never against the truth - and the paper then says the laboratory measurements are themselves `often subject to measurement errors`
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mechanisms[6]
mechanismAND THE VALIDATION HAS ITS OWN SAMPLING PROBLEM: `It is important to have a random selection of individuals so that results from the validation can be generalized to any group of participants. When the sampling approach is not random and subjective, the results from the validation study can only apply to the same group of individuals.` A correction factor inherits the scope of the study that produced it - polman2011's MAGNIMS refusal, in a different discipline
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mechanisms[7]
mechanismAND A WHOLE DESIGN IS DEMOTED ON THE STRENGTH OF ONE BIAS: `bias often accompanies results from case-control studies. Hence, case-control studies can be conducted in order to generate a research hypothesis, but not to evaluate prognoses or treatment effects.` A study design assigned a permitted use - the same move donnelly2019 makes about a definition, here made about a method
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mechanisms[8]
mechanismAND THE ONE FIELD THAT DOES ADJUST FOR MEASUREMENT ERROR IS NAMED: `Measurement error bias has rarely been discussed or adjusted for in the medical research literature, except in the field of forensic medicine, where forensic toxicologists have undoubtedly the most theoretical understanding of measurement bias as it is particularly relevant for their type of research.` Where a number has to survive cross-examination, its error is modelled - the practice follows the consequence, not the methodology
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mechanisms[9]
mechanismAND THE AGREEMENT BETWEEN SELF-REPORT AND RECORDS IS DISEASE-SPECIFIC: `a high agreement was found between self-reported questionnaires and medical record diagnoses of diseases such as diabetes, hypertension, myocardial infarction, and stroke but not for heart failure.` The instrument is not uniformly valid across the things it measures, which is exactly what prigerson2009's differential item functioning analysis tests for and deletes
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mechanisms[10]
extends the existing bias literature, which the paper says has discussed types of bias while 'the problem of bias is still frequently ignored in practice'
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claim_relations[0]
critiques the naive analysis - the standard practice of ignoring measurement error, which yields 'inconsistent (or biased) and/or inefficient estimators'
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claim_relations[1]
critiques the use of case-control studies for anything beyond hypothesis generation, given that 'bias often accompanies results from case-control studies'
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claim_relations[2]
extends forensic toxicology, named as the one field with 'the most theoretical understanding of measurement bias', from which medical research is asked to learn
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claim_relations[3]
sense`bias` - `any systematic error in the design, conduct, or analysis of a study`; split by WHERE it enters into `selection bias` and `information bias`
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senses[0]
sense`information bias` - `otherwise known as misclassification`; error originating in `the approach that is utilized to obtain or confirm study measurements`
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senses[1]
sense`measurement error` = `instrumental error` = `measurement imprecision` = `measurement bias` - four names presented as interchangeable in one sentence, with no criterion for choosing between them
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senses[2]
sense`confirmation bias` - `otherwise known as confirmatory, ascertainment, or observer bias`: `a decision is made according to the subject's preconceptions, beliefs, or preferences`
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senses[3]
sense`naive analysis` - `An analysis that ignores the effect of measurement error on the results`. A name given to the default practice so that it can be argued against
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senses[4]
sense`systematic` vs `random` error - systematic deviations are `either consistently higher or lower than the true values`; random ones are `not consistent`, follow a distribution, and are called `multiplicative or non-gaussian` when proportional to the measurement
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senses[5]
controlvalidating a self-reporting instrument against laboratory measurements (internal) or medical records and family report (external) before using it — partial
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— rules out social desirability bias in answers about drug use, income, diet or violence
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controls[0]
controlrequiring the validation sample to be randomly selected — held
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— rules out a correction factor that applies only to the group it was derived from
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controls[1]
controlchoosing a short recall period, stratifying it by how many events a participant had, and using memory aids and diaries — partial
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— rules out recall error scaling with the length of the period being remembered
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controls[2]
controlblinding or masking, single or double, and independent checks across laboratories — partial
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— rules out confirmation bias in any step that requires human judgment
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controls[3]
weightthe existing bias literature, which the paper says has discussed types of bias while 'the problem of bias is still frequently ignored in practice' — 0.9
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claim_relations[0]
weightthe naive analysis - the standard practice of ignoring measurement error, which yields 'inconsistent (or biased) and/or inefficient estimators' — 0.9
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claim_relations[1]
weightthe use of case-control studies for anything beyond hypothesis generation, given that 'bias often accompanies results from case-control studies' — 0.85
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claim_relations[2]
weightforensic toxicology, named as the one field with 'the most theoretical understanding of measurement bias', from which medical research is asked to learn — 0.85
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claim_relations[3]
notemeasurement error —same-as→ measurement bias — 'If these errors…' Four names, and they are not obviously synonyms: instrumental error names a source, imprecision names a property of the instrument, and measurement bias names what the error does to the estimate. Tagged as the paper states it, and the un-sorted list is recorded as a limitation
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concept_edges[0]
relationmeasurement error —same-as→ measurement bias — measurement error, instrumental error, measurement imprecision and measurement bias are offered as names for one thing, in one sentence, with no test for telling them apart
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concept_edges[0]
weightmeasurement error —same-as→ measurement bias — 0.9
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concept_edges[0]
noteconfirmation bias —same-as→ observer bias — 'Placing emphasis…' The second four-name list in one review. In ordinary use 'observer bias' is the observer's expectation affecting a measurement and 'confirmation bias' is a search strategy over evidence; here they are one entry
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concept_edges[1]
relationconfirmation bias —same-as→ observer bias — confirmation, confirmatory, ascertainment and observer bias are given as names for deciding according to one's preconceptions
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concept_edges[1]
weightconfirmation bias —same-as→ observer bias — 0.9
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concept_edges[1]
noteBias —operationalised-as→ any systematic error in the design, conduct, or analysis of a study — 'Bias can be defined…' Systematic is doing all the work - the definition excludes random error by construction, and the paper then spends its longest section on random measurement error, which is systematic in its effect on an estimate
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concept_edges[2]
relationBias —operationalised-as→ any systematic error in the design, conduct, or analysis of a study — bias is defined as systematic error anywhere in a study, which is what allows the split by point of entry rather than by effect
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concept_edges[2]
weightBias —operationalised-as→ any systematic error in the design, conduct, or analysis of a study — 0.95
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concept_edges[2]
noteselection bias —distinct-from→ information bias — 'In health studies…' A clean two-way split with a stated criterion, which is more than most of the taxonomies in this corpus manage
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concept_edges[3]
relationselection bias —distinct-from→ information bias — the two sources are the approach to choosing subjects and the approach to measuring them - a partition by point of entry, not by consequence
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concept_edges[3]
weightselection bias —distinct-from→ information bias — 0.95
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concept_edges[3]
noterandom sampling —distinct-from→ measurement error variability — 'However, RANDOM…' The warning is printed because the conflation is common, and the cost is specific: the confidence interval looks complete and covers one of two error sources
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concept_edges[4]
relationrandom sampling —distinct-from→ measurement error variability — sampling variability is handled by standard statistical methods and measurement error uncertainty is not, so treating them as one hides the second
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concept_edges[4]
weightrandom sampling —distinct-from→ measurement error variability — 0.95
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concept_edges[4]
noteAn analysis that ignores the effect of measurement error on the results —same-as→ a naïve analysis — 'An analysis that…' Naming the default is how an omission becomes visible. Compare carroll2007's 'thinness' for missing fidelity data: both fields coined a word for a thing that is not there
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concept_edges[5]
relationAn analysis that ignores the effect of measurement error on the results —same-as→ a naïve analysis — the ordinary analysis, done without accounting for measurement error, is given a name - and the name is the argument
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concept_edges[5]
weightAn analysis that ignores the effect of measurement error on the results —same-as→ a naïve analysis — 0.9
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concept_edges[5]
notecases —contrasted-with→ healthy controls — 'study, CASES…' The bias is not noise; it has a sign that follows from the design, which is why the paper can say what it does to the odds ratio
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concept_edges[6]
relationcases —contrasted-with→ healthy controls — cases recall exposure more readily than controls, so the same true history is reported at different rates on the two sides of the comparison
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concept_edges[6]
weightcases —contrasted-with→ healthy controls — 0.95
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concept_edges[6]
noteThe size of the difference between the observed rates of exposure to risk factors in cases and controls —causes→ the observed odds ratio — 'The size of the difference…' A named error class traced all the way to the published statistic - three steps, each stated, which is what makes recall bias adjustable rather than merely deplorable
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concept_edges[7]
relationThe size of the difference between the observed rates of exposure to risk factors in cases and controls —causes→ the observed odds ratio — the recall asymmetry inflates the observed exposure difference, and the inflated difference raises the odds ratio the study reports
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concept_edges[7]
weightThe size of the difference between the observed rates of exposure to risk factors in cases and controls —causes→ the observed odds ratio — 0.95
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concept_edges[7]
outcomecontrol — partial
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controls[0]
outcomecontrol — held
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controls[1]
outcomecontrol — partial
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controls[2]
outcomecontrol — partial
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controls[3]
limitationTHE FOUR NAMES ARE LISTED AND NEVER SEPARATED. `measurement error, instrumental error, measurement imprecision, or measurement bias` are offered as one thing; `confirmatory, ascertainment, or observer bias` likewise. Some of these are not synonyms in ordinary use - *instrumental* error is a subclass of measurement error, and *observer* bias is usually distinguished from *confirmation* bias by who holds the belief. A review of error that hands the reader an unsorted synonym list is reproducing the problem it describes
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limitations[0]
limitationAND THE ADVICE IS ADMITTED TO BE HARD TO FOLLOW: `Bias is often not accounted for in practice. Even though a number of adjustment and prevention methods ... are available, applying them can be rather challenging due to limited time and resources. For example, measurement error bias properties might be difficult to detect, particularly if there is a lack of information about the measuring instrument.` The adjustment methods all require knowing the error distribution, and the error distribution requires a validation study that is `expensive`
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limitations[1]
limitationAND THE VALIDATION CHAIN BOTTOMS OUT ON ANOTHER MEASUREMENT: laboratory measurements are the recommended standard for validating self-report, and they are `often subject to measurement errors` - `in a validation study to estimate sodium intake using a 24-hour urinary excretion method, the estimated sodium intake tended to be lower than the true amount`. There is no step in the chain that touches the true value, and the paper says so about its own recommended remedy
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limitations[2]
limitationAND CONFIRMATION BIAS IS THE ONE WITH NO ADJUSTMENT METHOD AT ALL: the remedies offered are independent checks, blinding, and `specific education and training programs`, and the conclusion is `it is important to accept that bias is a part of investigations. Quantifying this inevitable bias and its potential sources must be part of well-developed conclusions.` The paper's title promises adjustment methods; for the third of its three biases there are none
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limitations[3]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
finding
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content_tags.findings[0]
finding
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content_tags.findings[1]
finding
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content_tags.findings[2]
finding
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content_tags.findings[3]
finding
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content_tags.findings[4]
finding
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content_tags.findings[5]
finding
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content_tags.findings[6]
finding
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content_tags.findings[7]
finding
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content_tags.findings[8]
quantity3 - self-reporting bias (with social desirability and recall as subtypes), measurement error bias, confirmation bias —
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content_tags.quantities[1]
quantityexposure underreported in controls, overreported in cases; the odds ratio rises —
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content_tags.quantities[2]
variablesystematic error
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content_tags.variables[0]
variablerandom error
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content_tags.variables[1]
variablerecall period
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content_tags.variables[2]
definitionbias
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content_tags.definitions[0]
definitioninformation bias
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content_tags.definitions[1]
definitionmeasurement error
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content_tags.definitions[2]
definitionconfirmation bias
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content_tags.definitions[3]
definitionnaïve analysis
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content_tags.definitions[4]
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptBias — not defined in this paper's own words; the genus - any systematic error in a study
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concepts[0]
conceptany systematic error in the design, conduct, or analysis of a study — not defined in this paper's own words; the definition given for it
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concepts[1]
conceptselection bias — not defined in this paper's own words; error entering through who is studied
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concepts[2]
conceptinformation bias — not defined in this paper's own words; error entering through how they are measured
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concepts[3]
conceptmeasurement error — not defined in this paper's own words; the first of the four names for one thing
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concepts[4]
conceptmeasurement bias — not defined in this paper's own words; the fourth - and the only one that names the consequence rather than the cause
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concepts[5]
conceptconfirmation bias — not defined in this paper's own words; deciding by preconception
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concepts[6]
conceptobserver bias — not defined in this paper's own words; one of the three names offered for the same thing
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concepts[7]
conceptrandom sampling — not defined in this paper's own words; the variability the standard toolbox does handle
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concepts[8]
conceptmeasurement error variability — not defined in this paper's own words; the variability it does not
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concepts[9]
conceptAn analysis that ignores the effect of measurement error on the results — not defined in this paper's own words; the default practice
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concepts[10]
concepta naïve analysis — not defined in this paper's own words; the name it is given so that it can be argued against
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concepts[11]
conceptcases — not defined in this paper's own words; the group that recalls exposure more readily
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concepts[12]
concepthealthy controls — not defined in this paper's own words; the group that recalls it less
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concepts[13]
conceptThe size of the difference between the observed rates of exposure to risk factors in cases and controls — not defined in this paper's own words; the quantity recall bias inflates
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concepts[14]
conceptthe observed odds ratio — not defined in this paper's own words; what that inflation reaches
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concepts[15]
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
a validation chain with no true value at the end of it
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content_tags.statistics[0]
what the standard toolbox silently does not cover
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content_tags.statistics[1]

When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

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