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Valid (logical) argument
| Glossary of Statistical Terms |
A valid logical argument is one in which the truth of the premises indeed guarantees the truth of the conclusion. for example, the following logical argument is valid: if the forecast calls for rain, i will not wear sandals. the forecast calls for rain. therefore, i will not wear sandals. this argument has two premises which, together, guarantee the truth of the conclusion. an argument can be logically valid even if its premises are false. see also invalid argument and sound argument.
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Conclusion, английский
- Вывод;
- Вывод; умозаключение
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Denying the consequent, английский
A valid logical argument that concludes from the premise a → b and the premise !b that therefore, !a. the name comes from the fact that the operation denies (i.e., asserts the logical negation) the consequent (b) in the conditional.
Selection bias, английский
- A systematic tendency for a sampling procedure to include and/or exclude units of a certain type. for example, in a quota sample, unconscious prejudices or predilections on the part of the interviewer can result in selection bias. selection bias is a potential problem whenever a human has latitude in selecting individual units for the sample; it tends to be eliminated by probability sampling schemes in which the interviewer is told exactly whom to contact (with no room for individual choice).
- A systematic difference between a target population and the sample of data. for example, data mining on customer complaint databases has a selection bias in that the data only capture customers who were motivated and able to contact the manufacturer about their complaint and does not capture complaints or customers who were not motivated nor able. self organizing map (som) a self organizing map is a form of kohonen network that arranges its clusters in a (usually) two-dimensional grid so that the codebook vectors (the cluster centers) that are close to each other on the grid are also close in the k-dimensional feature space. the converse is not necessarily true, as codebook vectors that are close in feature-space might not be close on the grid. the map is similar in concept to the maps produced by descriptive techniques such as multi-dimensional scaling (mds) self-stabilization a property of neural networks (e.g., art models). as a network trains itself on a finite number of cases, the various weights should gradually stabilize. see also: art, ftp:://ftp.sas.com/pub/neural/faq2.html, http://www.wi.leidenuniv.nl/art/.
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