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We do not intend the term "unlikely" to imply an event will not happen. We use "probably" and "likely" to indicate there is a greater than even chance. We use words such as "we cannot dismiss", "we cannot rule out", and "we cannot discount" to reflect an unlikely—or even remote—event whose consequences are such it warrants mentioning.
In both examples, as the number of comparisons increases, it becomes more likely that the groups being compared will appear to differ in terms of at least one attribute. Our confidence that a result will generalize to independent data should generally be weaker if it is observed as part of an analysis that involves multiple comparisons, rather ...
The law of truly large numbers (a statistical adage), attributed to Persi Diaconis and Frederick Mosteller, states that with a large enough number of independent samples, any highly implausible (i.e. unlikely in any single sample, but with constant probability strictly greater than 0 in any sample) result is likely to be observed. [1]
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]
Unlikely to occur, but possible The failure mode may then be charted on a criticality matrix using severity code as one axis and probability level code as the other. For quantitative assessment, modal criticality number C m {\displaystyle C_{m}} is calculated for each failure mode of each item, and item criticality number C r {\displaystyle C ...
As more data are observed, instead of being used to make independent estimates, they can be combined with the previous samples to make a single combined sample, and that large sample may be used for a new maximum likelihood estimate. As the size of the combined sample increases, the size of the likelihood region with the same confidence shrinks.
For example, the problem of deciding whether a graph G contains H as a minor, where H is fixed, can be solved in a running time of O(n 2), [25] where n is the number of vertices in G. However, the big O notation hides a constant that depends superexponentially on H .
Just as "reward" was commonly used to alter behavior long before "reinforcement" was studied experimentally, the Premack principle has long been informally understood and used in a wide variety of circumstances. An example is a mother who says, "You have to finish your vegetables (low frequency) before you can eat any ice cream (high frequency)."