Editing Misuse of statistics (section)

==Definition, limitations and context==
One usable definition is: "Misuse of Statistics: Using numbers in such a manner that – either by intent or through ignorance or carelessness – the conclusions are unjustified or incorrect."{{sfn | Spirer | Spirer| Jaffe | 1998 |p=1}} The "numbers" include [[misleading graph]]ics discussed in other sources.  The term is not commonly encountered in statistics texts and there is no single authoritative definition. It is a generalization of [[How to Lie with Statistics|lying with statistics]] which was richly described by examples from statisticians 60 years ago.

The definition confronts some problems (some are addressed by the source):<ref>{{cite journal
 | last1 = Gardenier | first1 = John |first2=David|last2= Resnik
 | title = The misuse of statistics: concepts, tools, and a research agenda
 | journal = Accountability in Research: Policies and Quality Assurance | volume = 9|issue=2 | pages = 65–74|pmid=12625352
 | year = 2002 | doi=10.1080/08989620212968| s2cid = 24167609 }}</ref>

# Statistics usually produces probabilities; conclusions are provisional
# The provisional conclusions have errors and error rates.  Commonly 5% of the provisional conclusions of significance testing are wrong
# Statisticians are not in complete agreement on ideal methods
# Statistical methods are based on assumptions which are seldom fully met
# Data gathering is usually limited by ethical, practical and financial constraints.

''[[#CITEREFHuff1954|How to Lie with Statistics]]'' acknowledges that statistics can ''legitimately'' take many forms.  Whether the statistics show that a product is "light and economical" or "flimsy and cheap" can be debated whatever the numbers.  Some object to the substitution of statistical correctness for moral leadership (for example) as an objective.  Assigning blame for misuses is often difficult because scientists, pollsters, statisticians and reporters are often employees or consultants.

An insidious misuse of statistics is completed by the listener, observer, audience, or juror.  The supplier provides the "statistics" as numbers or graphics (or before/after photographs), allowing the consumer to draw conclusions that may be unjustified or incorrect. The poor state of public [[statistical literacy]] and the non-statistical nature of human intuition make it possible to mislead without explicitly producing faulty conclusion.  The definition is weak on the responsibility of the consumer of statistics.

A historian listed over 100 fallacies in a dozen categories including those of generalization and those of causation.<ref>{{cite book | last = Fischer | first = David | title = Historians' fallacies: toward a logic of historical thought | publisher = Harper & Row | location = New York | year = 1979 | isbn = 978-0060904982 | pages = 337–338 }}</ref>  A few of the fallacies are explicitly or potentially statistical including sampling, statistical nonsense, statistical probability, false extrapolation, false interpolation and insidious generalization.  All of the technical/mathematical problems of applied probability would fit in the single listed fallacy of statistical probability.  Many of the fallacies could be coupled to statistical analysis, allowing the possibility of a false conclusion flowing from a statistically sound analysis.

An example use of statistics is in the analysis of medical research.  The process includes<ref>{{cite journal
 | last = Strasak | first = Alexander M. |author2=Qamruz Zaman |author3=Karl P. Pfeiffer |author4=Georg Göbel |author5=Hanno Ulmer
  | title = Statistical errors in the medical research-a review of common pitfalls
 | journal = [[Swiss Medical Weekly]] | volume = 137 | issue = 3–4 | pages = 44–49 |pmid=17299669
 | year = 2007 | doi = 10.4414/smw.2007.11587 }}  In this article anything less than the best statistical practice is equated to the potential misuse of statistics.  In a few pages 47 potential statistical errors are discussed; errors in study design, data analysis, documentation, presentation and interpretation.  "[S]tatisticians should be involved early in study design, as mistakes at this point can have major repercussions, negatively affecting all subsequent stages of medical research."</ref><ref name="Indrayan2007">{{cite journal|last1=Indrayan|first1=Abhaya|title=Statistical fallacies in orthopedic research|journal=Indian Journal of Orthopaedics|volume=41|issue=1|year=2007|pages=37–46|doi=10.4103/0019-5413.30524|doi-broken-date=1 November 2024|doi-access=free|pmid=21124681|pmc=2981893}} Contains a rich list of medical misuses of statistics of all types.</ref> experimental planning, the conduct of the experiment, data analysis, drawing the logical conclusions and presentation/reporting.  The report is summarized by the popular press and by advertisers.  Misuses of statistics can result from problems at any step in the process.  The statistical standards ideally imposed on the scientific report are much different than those imposed on the popular press and advertisers; however, cases exist of advertising disguised as science, such as [[Australasian Journal of Bone & Joint Medicine]].  The definition of the misuse of statistics is weak on the required completeness of statistical reporting.  The opinion is expressed that newspapers must provide at least the source for the statistics reported.