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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.
[3] Using an audit protocol tool, it was identified that human entry errors range from 0.01% when entering donors' clinical follow-up details, to 0.53% when entering pathological details, highlighting the importance of an audit protocol tool in a medical research database.
A type II error, or a false negative, is the erroneous failure in bringing about appropriate rejection of a false null hypothesis. [1] Type I errors can be thought of as errors of commission, in which the status quo is erroneously rejected in favour of new, misleading information. Type II errors can be thought of as errors of omission, in which ...
Research integrity or scientific integrity became an autonomous concept within scientific ethics in the late 1970s. In contrast with other forms of ethical misconducts, the debate over research integrity is focused on "victimless offence" that only hurts "the robustness of scientific record and public trust in science". [3]
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 ...
There is no single definition of diagnostic error, reflecting in part the dual nature of the word diagnosis, which is both a noun (the name of the assigned disease; diagnosis is a label) and a verb (the act of arriving at a diagnosis; diagnosis is a process).
The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present.
Selection bias is the bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population intended to be analyzed. [1]