Selective reporting and misrepresentation of data undermine the integrity of academic research. Selective reporting refers to intentionally presenting or omitting certain information, data, or results in a biased manner to support a particular viewpoint. There are several types of selective reporting and misrepresentation, including publication bias, outcome reporting bias, data dredging, spin, and selective citation. Upholding honesty, objectivity, and integrity in experimental design, data analysis, and reporting is important. Fabrication, falsification, or misrepresentation of data is unethical. Journals should verify consent forms and data sources if concerns about accuracy or legitimacy arise.

1. SELECTIVE REPORTING AND
MISREPRESENTATION OF DATA
DR.ANJALI UPADHYE, PH.D.
STATSANJAL STATISTICAL CONSULTANCY SERVICES,SANGLI

2. SELECTIVE REPORTING
Selective reporting bias is when results from scientific research are
deliberately not fully or accurately reported, in order to suppress negative or
undesirable findings. The end result is that the findings are not reproducible,
because they have been skewed by bias during the analysis or writing stages.
Selective reporting is one type of bias which undermines the integrity of
academic research. It is a large contributor to the current ‘reproducibility
crisis’ facing scientific publishing
Selective reporting is important, and many people still ignore the issue. And
it's one of the root causes of the current replicability crisis we are facing not
only in biomedical sciences, in the social sciences, but it's clear that it's also
happening in other types of sciences.

3. FURTHER MORE ….
• Selective reporting refers to the act of intentionally presenting or omitting
certain information, data or results in a biased manner to support a particular
viewpoint, hypothesis or conclusion.
• This can be done by presenting only the information that supports a
particular argument or by ignoring information that contradicts it.

4. TYPES OF SELECTIVE
REPORTING
• Publication bias: This occurs when studies with significant or positive results are more likely to be
published than studies with non-significant or negative results. This can lead to an overestimation of
the true effect size and can skew the scientific literature.
• Outcome reporting bias: This occurs when only certain outcomes of a study are reported, while others
are not. This can occur when the reported outcomes are more favorable to the author's hypothesis or
agenda.
• Data dredging: This occurs when multiple statistical tests are performed on a dataset to find
significant results, even if the results are not meaningful or relevant. This can lead to false-positive
results and can be a form of data manipulation.
• Spin: This occurs when the presentation of the results is biased or slanted towards a certain
interpretation or conclusion, even if the data do not fully support it. This can be a deliberate attempt to
manipulate the reader's perception of the results.
• Selective citation: This occurs when only certain studies or sources are cited to support a particular
argument, while other relevant studies or sources are ignored. This can be a form of cherry-picking
data to support a particular viewpoint.

5. AS PROFESSOR BOUTER EXPLAINS, SELECTIVE REPORTING BIAS CAN
INCORPORATE A NUMBER OF OTHER TYPES OF BIAS, SUCH AS :
It refers to the manipulation or distortion of data to create a false
or misleading impression.
This can be done by selectively choosing data, altering or
omitting data points, or presenting data in a way that obscures
the true meaning or significance of the information.

6. Selective reporting bias, FFP, and
other examples of research
misconduct, all contribute to a culture
of mistrust in science and academia.
However, journal editors can play a
role in helping change this
perception, by upholding a culture of
research integrity on their journals.

9. “Scientific misconduct includes (negligent or
intended) fabrication (making up data or results),
falsification (changing or misreporting research
data or improper manipulation of experiments)
and plagiarism (using ideas or words without
accurate reference). These practices go against all
scientific values and can undermine the scientific
progress. Even more, it can cause harm.”
Science Europe. ‘Research Integrity Practices in
Science Europe Member Organisations: Survey
Report’. Science Europe, July 2016.

10.  Publication is not simply the reporting of facts arising from a
straightforward analysis thereof.
 Authors have broad latitude when writing their reports and may be
tempted to consciously or unconsciously “spin” their study
findings.
 Spin has been defined as a specific intentional or unintentional
reporting that fails to faithfully reflect the nature and range of
findings and that could affect the impression the results produce
in readers.

11. Spin is defined as a specific reporting that fails to faithfully reflect the nature and
range of findings and that could affect the impression that the results produce in
readers, a way to distort science reporting without actually lying.
Spin could be unconscious and unintentional. Reporting results in a manuscript
implies some choices about which data analyses are reported, how data are reported,
how they should be interpreted, and what rhetoric is used.
These choices, which can be legitimate in some contexts, in another context can
create an inaccurate impression of the study results. It is almost impossible to
determine whether spin is the consequence of a lack of understanding of
methodologic principles, a parroting of common practices, a form of unconscious
behavior, or an actual willingness to mislead the reader.
However, spin, when it occurs, often favours the author’s vested interest (financial,
intellectual, academic, and so forth)

12. Falsification Fabrication
Falsification is the changing or omission of
research results (data) to support claims,
hypotheses, other data, etc. Falsification
can include the manipulation of research
instrumentation, materials, or processes.
Manipulation of images or representations
in a manner that distorts the data or “reads
too much between the lines” can also be
considered falsification.
Fabrication is the construction and/or
addition of data, observations, or
characterizations that never occurred in the
gathering of data or running of
experiments. Fabrication can occur when
“filling out” the rest of experiment runs, for
example. Claims about results need to be
made on complete data sets (as is normally
assumed), where claims made based on
incomplete or assumed results is a form of
fabrication.

13. THE CONCEPT
OF ‘MI srepresentation,’ unlike ‘fabrication’ and ‘falsification,’ is neither clear nor
uncontroversial. Most scientists will agree that fabrication is making up data and falsification
is changing data. But what does it mean to misrepresent data? As a minimal answer to this
question, one can define ‘misrepresentation of data’ as ‘communicating honestly reported
data in a deceptive manner.’ But what is deceptive communication? The use of statistics
presents researchers with numerous opportunities to misrepresent data. For example, one
might use a statistical technique, such as multiple regression or the analysis of variance, to make
one's results appear more significant or convincing than they really are. Or one might eliminate
ng data include drawing
nd
, a using suggestive
(or trim) outliers when ‘cleaning up raw data. Other ways of misrepresenti
unwarranted inference from data, creating deceptive graphs of figures
language for rhetorical effect. However, since researchers often disagree about the proper use of
statistical techniques and other means of representing data, the line between
misrepresentation of data and ‘disagreement about research methods’ is often blurry.
Since ‘misrepresentation’ is difficult to define, many organizations have refused to characterize
misrepresenting data as a form of scientific misconduct. On the other hand, it is important to call
attention to the problem of misrepresenting data, if one is concerned about promoting objectivity
in research, since many of science's errors and biases result from the misrepresentation of data.
Resnik, D.B.. (2015). Objectivity of Research: Ethical Aspects. 10.1016/B978-0-08-097086-8.11019-0.

14. ROOTS OF
SELECTIVE
REPORTING
AND
MISREPRESE
NTATION OF
DAT
 Departmental publishing requirements.
 Requirements for promotion.
 Competitive pressures.
 Institutional, regional, and national recognition.
 Financial remuneration.
 Media publicity.
 Inadequate data management practices/policies
and storage resources
 Time pressure
 Researchers do not feel well equipped or
knowledgeable about how to publish their data
 Legal and ethical concerns

15. TYPES OF MISREPRESENTATION
OF DATA
• Data falsification: This occurs when data is intentionally altered or fabricated to support a
particular hypothesis or conclusion. This is a serious ethical violation and can have
severe consequences for the individual and the organization involved.
• Data cherry-picking: This occurs when only certain data points are selected or highlighted
to support a particular conclusion, while other data points are ignored or de-emphasized.
This can lead to a biased or incomplete picture of the overall data.
• Data manipulation: This occurs when data is manipulated or adjusted in a way that alters
the conclusions that can be drawn from it. This can include changing the scale of the axis
on a graph to make differences appear larger or smaller than they actually are.
• Data misinterpretation: This occurs when data is presented in a way that is misleading or
misinterpreted. This can include presenting correlation as causation, or failing to
acknowledge alternative explanations for the data.
• Data omission: This occurs when relevant data is left out of a report or analysis, either
intentionally or unintentionally. This can lead to an incomplete or inaccurate picture of the
overall data.

16. HONESTY
, OBJECTIVITY AND
INTEGRITY
Honesty, objectivity and integrity and avoiding bias in experimental
design, data
analysis, data interpretation, and reporting data, results, methods and
procedures in all scientific communications are optimal for research.
Fabrication, falsification, or misrepresentation of data is plainly unethical
and should not be resorted to. Trimming outliers from a data set without
providing reasons or using an unsuitable statistical technique to enhance
the significance of results is unethical and not permitted.

17. HONESTY
, OBJECTIVITY AND
INTEGRITY
 Honesty (valid interpretations and justifiableclaims)
 Reliability (in performing and reporting research)
 Objectivity (transparency and verifiability)
 Impartiality and independence (from pressures and interests)
 Open communication (ensuring availability and accessibility)
 Duty of care (for research subjects – e.g. human subjects, experimental animals)
 Fairness (referencing, crediting,relationship with colleagues)
 Responsibility for future science generations (mentorship)

18. ETHICAL ISSUES OF DATA
F
R
ailin
E
g to
P
incl
O
ude n
R
umb
T
er o
If N
eligib
G
le
participants.
Data ‘‘dredging”.
Inaccurate reporting of missing data points Splitting data into multiple publications
Failing to report all pertinent data. Inappropriate use of terminology without
precise definitions.
Failing to report negative results Reporting conclusions that are not supported by
data.
Allowing research sponsors to influence
reporting of results.
Ignoring citations or prior work that challenge
stated conclusions or call current findings into
question.
Inappropriate graph labels. Inflation of research results for the media.
Reporting percentages rather than actual
numbers.
Reporting results of inappropriately applied
statistical tests.
Reporting no difference, when power is
inadequate.

19. Several steps may be taken toward ensuring the scientifically and ethically most valid
reporting methods.
One method is the advance determination of the most appropriate statistical and
reporting techniques. Some advocate that a research paper can be written in large
part prior to data collection, with only specific numbers missing, to be filled in after
data collection.
A carefully formulated research question and study design enables the investigator to
establish scientifically valid statistical analysis, possible results, and conclusions,
prior to the potential influence of external forces on reporting methodology.
Researchers not only should take care to avoid every aspect of scientific misconduct in
research, but should take responsibility for mentoring young investigators regarding
appropriate scientific conduct, and for reporting and investigating alleged scientific
misconduct.
Researchers should be aware of and support institutional compliance programs that
help to promote accurate and honest research.

22. CASE TEXT (ANONYMISED):
A research paper was submitted to our journal and underwent several rounds of peer review
and editorial curation. We were on the point of acceptance when we realised there were some
images that were submitted along with the paper where patients were perfectly identifiable but
we did not have the signed informed consent forms. We therefore asked the authors for the
consent forms (corresponding to about 10 different patients). We received these on the same
day of the request. Although we don’t usually question the veracity of the consent forms, in this
case all the writing on the forms seemed to come from the same person, the dates were in the
same hand, and all were dated on the same day that we asked the authors for the form. It
therefore seems highly unlikely that they did not have the forms beforehand. Some of the
signatures, although corresponding in theory to different people, also seemed very alike. We
also felt it was suspicious that the authors were able to return all the forms on the same day,
since the authors are from a hospital and it usually may take days or weeks to chase down one
patient, let alone 10, given the geographical spread.
Questions for the Forum
What course of action should we take?
Do we simply accept the forms as being legitimate, knowing we have no ability or resources to
investigate these matters?

23. The Journal of Alternative and Complementary MedicineVol. 7, No. 1Case Study
A Case Study of Misrepresentation of the Scientific Literature: Recent Reviews of Chiropractic
Joseph Morley
, Anthony L. Rosner
, and Daniel Redwood
Published Online:5 Jul 2004https://doi.org/10.1089/107555301300004547
PDF/EPUB
Permissions & Citations
Share
Abstract
Accurate use of published data and references is a cornerstone of the peer-review process. Statements, inferences, and conclusions
based upon these references should logically ensue from the data they contain. When journal articles and textbook chapters
summarizing the safety and efficacy of particular therapies or interventions use references inaccurately or with apparent intent to
mislead, the integrity of scientific reporting is fundamentally compromised.
Ernst et al.'s publication on chiropractic include repeated misuse of references, misleading statements, highly selective use of certain
published papers, failure to refer to relevant literature, inaccurate reporting of the contents of published work, and errors in citation.
Meticulous analysis of some influential negative reviews has been carried out to determine the objectivity of the data reported. The
misrepresentation that became evident deserves full debate and raises serious questions about the integrity of the peer-review process
and the nature of academic misconduct.
Reference : https://www.liebertpub.com/doi/10.1089/107555301300004547

24. https://www.jstor.org/stable/3564474

25. HOW TO AVOID SELECTIVE REPORTING AND MISREPRESENTATION OF DATA
• Ensure transparency: Be open and honest about the data being presented, including any
limitations or weaknesses. This can help to avoid the perception of bias or manipulation.
• Avoid cherry-picking: Present all relevant data, even if it does not support the hypothesis or
conclusion being tested. This can help to ensure that the overall picture is not skewed.
• Use appropriate statistical methods: Ensure that appropriate statistical methods are used to
analyze the data and that the results are presented in a way that accurately reflects the data.
• Verify data sources: Verify the data sources and ensure that they are reliable and accurate.
This can help to avoid errors or biases in the data.
• Use independent review: Have the data and analysis reviewed by independent experts to
ensure that the conclusions are sound and unbiased.
• Follow ethical standards: Adhere to ethical standards and guidelines for data reporting and
analysis, including those set forth by professional organizations and regulatory bodies.
• Acknowledge limitations: Be transparent about the limitations of the study or analysis and
acknowledge any potential sources of bias or error. This can help to ensure that the data is
interpreted accurately and responsibly.

26.  Bailar JC (2006) How to distort the scientific record without actually lying: Truth, and arts of
science. Eur J Oncol 11:217–224.
 Fletcher RH, Black B (2007) “Spin” in scientific writing: Scientific mischief and legal jeopardy.
Med Law 26:511–525.
 https://publicationethics.org/guidance/Case


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