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Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical and social sciences, humanities, [2] and business ...
Another key example of observer bias is a 1963 study, "Psychology of the Scientist: V. Three Experiments in Experimenter Bias", [9] published by researchers Robert Rosenthal and Kermit L. Fode at the University of North Dakota. In this study, Rosenthal and Fode gave a group of twelve psychology students a total of sixty rats to run in some ...
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 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").
Random errors are errors in measurement that lead to measurable values being inconsistent when repeated measurements of a constant attribute or quantity are taken. Random errors create measurement uncertainty. Systematic errors are errors that are not determined by chance but are introduced by repeatable processes inherent to the system. [3]
In the philosophy of science, underdetermination or the underdetermination of theory by data (sometimes abbreviated UTD) is the idea that evidence available to us at a given time may be insufficient to determine what beliefs we should hold in response to it. [1]
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 ...
If the probability of obtaining a result as extreme as the one obtained, supposing that the null hypothesis were true, is lower than a pre-specified cut-off probability (for example, 5%), then the result is said to be statistically significant and the null hypothesis is rejected.