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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").
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 ...
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 ...
In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. [1]
In predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model.It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways.
Research transparency is a major aspect of scientific research. It covers a variety of scientific principles and practices: reproducibility, data and code sharing, citation standards or verifiability. The definitions and norms of research transparency significantly differ depending on the disciplines and fields of research.
Producing the best available information from uncertain data remains the goal of researchers, tool-builders, and analysts in industry, academia and government. Their domains include data mining, cognitive psychology and visualization, probability and statistics, etc. Abductive reasoning is an earlier concept with similarities to ACH.
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.