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{{Theme
|Theme Type=Misconduct & Misbehaviors
|Has Parent Theme=Theme:48185295-9e1e-41fb-ab70-948596e588d5
|Title=Improper data use (a bias distorting research results)
|Is About=Researchers may handle data in a number of ways that can influence the results to become misleading.
|Important Because=Improper data use undermines the ethos of science and the corresponding misleading results can misguide and distort the production of knowledge.

Examples of improper data use include:

*'''Massaging''': … extensive transformations or other maneuvers to make inconclusive data appear … conclusive
*'''Extrapolating''': … predicting future trends based on unsupported assumptions …
*'''Smoothing''': discarding data points too far removed from expected … values
*'''Slanting''': … selecting certain trends in the data, … discarding others which do not fit …
*'''Fudging''': creating data points to augment incomplete data sets …
*'''Manufacturing''': creating entire data sets de novo, … <ref>Sindermann C. J. “Winning the games scientists play” (Plenum Press, NY, 1982)</ref>

Data dredging is looking for too many possible associations in a dataset to see of any of them are statistically significant. Data dredging results in false positive results.

“When a large number of associations can be looked at in a dataset where only a few real associations exist, a P value of 0.05 is compatible with the large majority of findings still being false positives.” <ref>Smith, George Davey, and Shah Ebrahim. "Data dredging, bias, or confounding: They can all get you into the BMJ and the Friday papers."</ref>

'''Origin of words'''

There are several terms describing the act of data dredging. These include: 

<br />

*"Data Dredging"<ref>Selvin, H. C., & Stuart, A. (1966). Data-dredging procedures in survey analysis. ''The American Statistician'', ''20''(3), 20-23.</ref>
*"Data Fishing"<ref>Grover, L. K., & Mehra, R. (2008). The lure of statistics in data mining. ''Journal of Statistics Education'', ''16''(1).</ref>
*“Data Snooping,”
*“P-hacking” 
<references />
|Important For=Principal investigators; Researchers; Policy makers; Supervisors; Postdocs; Journal publishers; Journal editors
|Has Best Practice====Related tools===
By Jensen (2000) <ref>Jensen, David. "Data Snooping, Dredging and Fishing: The Dark Side of Data Mining, A SIGKDD99 Panel Report." SIGKDD Explorations 1.2 (2000): 52-54.</ref>

*New data and cross-validation
*Sidak, Bonferroni, and other adjustments
*Resampling and randomization techniques

By Glenn & Cormier (2015) <ref>Suter, Glenn W., and Susan M. Cormier. "The problem of biased data and potential solutions for health and environmental assessments." Human and Ecological Risk Assessment: An International Journal 21.7 (2015): 1736-1752.</ref>

*Performing own reviews of the sources of data,
*Checking for retractions and corrections,
*Requiring full disclosure of methods,
*Acquiring original data and reanalyzing it,
*Avoiding secondary sources,
*Avoiding unreplicated studies or studies that are not concordant with related studies, and
*Checking for funding or investigator biases.

===Related cases===
Convenience, dichotomization, stratification, regression to the mean, impact of sample size, competing risks, immortal time and survivor bias, management of missing values . <ref>armona-Bayonas A, Jimenez-Fonseca P, Fernandez-Somoano A, et al. Top ten errors of statistical analysis in observational studies for cancer research. Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico. 2018;20(8):954-965.</ref> <ref>Reanalysis: Ebrahim S, Sohani Z, Montoya L, et al. Reanalyses of randomized clinical trial data. JAMA 2014;312:1024-32</ref>
<references />
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{{Tags
|Has Virtue And Value=Accuracy
|Has Good Practice And Misconduct=Questionable research practice
}}