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Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").
Falsification is manipulating research materials, equipment, or processes or changing or omitting data or results such that the research is not accurately represented in the research record. Plagiarism is the appropriation of another person's ideas, processes, results, or words without giving appropriate credit. One form is the appropriation of ...
Data often are missing in research in economics, sociology, and political science because governments or private entities choose not to, or fail to, report critical statistics, [1] or because the information is not available. Sometimes missing values are caused by the researcher—for example, when data collection is done improperly or mistakes ...
In scientific inquiry and academic research, data fabrication is the intentional misrepresentation of research results. As with other forms of scientific misconduct , it is the intent to deceive that marks fabrication as unethical, and thus different from scientists deceiving themselves .
In Denmark, scientific misconduct is defined as "intention[al] negligence leading to fabrication of the scientific message or a false credit or emphasis given to a scientist", and in Sweden as "intention[al] distortion of the research process by fabrication of data, text, hypothesis, or methods from another researcher's manuscript form or ...
How, then, can data ever be sufficient to prove a theory? This is the " epistemological problem of the indeterminacy of data to theory". The poverty of the stimulus argument and W.V.O. Quine 's 1960 'Gavagai' example are perhaps the most commented variants of the epistemological problem of the indeterminacy of data to theory.
When substituting for a data point, it is known as "unit imputation"; when substituting for a component of a data point, it is known as "item imputation". There are three main problems that missing data causes: missing data can introduce a substantial amount of bias , make the handling and analysis of the data more arduous , and create ...
In computer science, and in particular data management, uncertain data is commonplace and can be modeled and stored within an uncertain database; In optimization, uncertainty permits one to describe situations where the user does not have full control on the outcome of the optimization procedure, see scenario optimization and stochastic ...