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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. One form is the appropriation of ...
Sometimes missing values are caused by the researcher—for example, when data collection is done improperly or mistakes are made in data entry. [ 2 ] These forms of missingness take different types, with different impacts on the validity of conclusions from research: Missing completely at random, missing at random, and missing not at random.
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 ...
The publication or nonpublication of research findings, depending on the nature and direction of the results. Although medical writers have acknowledged the problem of reporting biases for over a century, [12] it was not until the second half of the 20th century that researchers began to investigate the sources and size of the problem of reporting biases.
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.
For example, if all that was known was that exactly $10 were spent on apples and oranges, and that apples cost $1 and oranges $2, then one would know enough to eliminate some possibilities (e.g., 6 oranges could not have been purchased), but one would not have enough evidence to know which specific combination of apples and oranges were purchased.
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.
The psychological literature has distinguished between several different forms of ambivalence. [4] One, often called subjective ambivalence or felt ambivalence, represents the psychological experience of conflict (affective manifestation), mixed feelings, mixed reactions (cognitive manifestation), and indecision (behavioral manifestation) in the evaluation of some object.