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Type i and type ii errors
| Glossary of Statistical Terms |
- These refer to hypothesis testing. a type i error occurs when the null hypothesis is rejected erroneously when it is in fact true. a type ii error occurs if the null hypothesis is not rejected when it is in fact false. see also significance level and power.
- Ошибки первого рода и ошибки второго рода. см. error (ошибка).
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Hypothesis, английский
- A suggested explanation for an observation or experimental result, which is then refined or disproved by further investigation
- Гипотеза
- N гипотеза critical period ~ гипотеза критического периода linguistic relativity ~ гипотеза лингвистиче- ской относительности (син. sapir–whorf ~) localist ~ локалистская гипотеза3 nostratic ~ ностратическая гипотеза sapir–whorf ~ гипотеза сепира–уорфа (син. linguistic relativity ~) single segment ~ гипотеза единого сегмента
- Гипотеза. положение, которое предположительно отражает истину или которое используется как основа для аргументации того, что еще не доказано.
- Гипотеза; предположение; допущение hypothesis-driven управляемый гипотезами
- Гипотеза, предположение
- An assertion, proposition or statement about relations or constraints whose truth-value is as of yet unknown .but in principle determinable by tests involving generally empirical but also logical evidence. hypotheses are generally derived from theories or models and when these theories have some validity of their own, they consititute predictions. l&qh
Significance, английский
- N значение, смысл (см. тж. signification)
- One of the arguments of the floor function.
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Control for a variable, английский
To control for a variable is to try to separate its effect from the treatment effect, so it will not confound with the treatment. there are many methods that try to control for variables. some are based on matching individuals between treatment and control; others use assumptions about the nature of the effects of the variables to try to model the effect mathematically, for example, using regression.
Axioms of probability, английский
There are three axioms of probability: (1) chances are always at least zero. (2) the chance that something happens is 100%. (3) if two events cannot both occur at the same time (if they are disjoint or mutually exclusive), the chance that either one occurs is the sum of the chances that each occurs. for example, consider an experiment that consists of tossing a coin once. the first axiom says that the chance that the coin lands heads, for instance, must be at least zero. the second axiom says that the chance that the coin either lands heads or lands tails or lands on its edge or doesn`t land at all is 100%. the third axiom says that the chance that the coin either lands heads or lands tails is the sum of the chance that the coin lands heads and the chance that the coin lands tails, because both cannot occur in the same coin toss. all other mathematical facts about probability can be derived from these three axioms. for example, it is true that the chance that an event does not occur is (100% − the chance that the event occurs). this is a consequence of the second and third axioms.
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