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As another example, two scientific devices could be considered independent sources of measurement data, unless they shared the same wiring or electrical power supply (or similar factors). Consulting multiple independent sources is a common technique for detecting errors and deception , as any divergences or contradictions between statements, or ...
A random sample can be thought of as a set of objects that are chosen randomly. More formally, it is "a sequence of independent, identically distributed (IID) random data points." In other words, the terms random sample and IID are synonymous. In statistics, "random sample" is the typical terminology, but in probability, it is more common to ...
Material available from sources that are self-published, primary sources, or biased because of a conflict of interest can play a role in writing an article, but it must be possible to source the information that establishes the subject's real-world notability to independent, third-party sources. Reliance on independent sources ensures that an ...
Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes.Two events are independent, statistically independent, or stochastically independent [1] if, informally speaking, the occurrence of one does not affect the probability of occurrence of the other or, equivalently, does not affect the odds.
A self-published source can be independent, authoritative, high-quality, accurate, fact-checked, and expert-approved. Self-published sources can be reliable, and they can be used (but not for third-party claims about living people). Sometimes, a self-published source is even the best possible source or among the best sources. For example:
Blocking reduces known but irrelevant sources of variation between units and thus allows greater precision in the estimation of the source of variation under study. Orthogonality Example of orthogonal factorial design Orthogonality concerns the forms of comparison (contrasts) that can be legitimately and efficiently carried out.
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In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population have a lower or higher sampling probability than others. It results in a biased sample [1] of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected. [2]