Selective Reporting and Misrepresentation of Data

“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.

 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.

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)

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.

The concept of ‘misrepresentation,’ 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
(or trim) outliers when ‘cleaning up raw data. Other ways of misrepresenting data include drawing
unwarranted inference from data, creating deceptive graphs of figures, and using suggestive
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.

ROOTS OF
SELECTIVE
REPORTING
AND
MISREPRESE
NTATION OF
DATA
 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

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.

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

ETHICAL ISSUES OF DATA
REPORTINGFailing to include number of eligible
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.

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.

 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


Selective Reporting and Misrepresentation of Data

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