The phrase "a correct type" is correct and usable in written English.
It can be used when referring to a specific category or classification that is accurate or appropriate in a given context.
Example: "In programming, it is essential to define a correct type for each variable to avoid errors."
Alternatives: "an appropriate type" or "a valid type".
The modeling results exhibited a correct type of morphology and pore/skeleton size in dependence on the initial polymer concentration and cooling temperature.
Simulation results based on either coalescent models or empirical population genetics data show that the QSAT has a correct type I error rate in the presence of population stratification and that the power of the QSAT is higher than that of family-based association designs.
Decidability of type checking is proved using this syntax directed version and a correct type checker can be extracted from this proof.
We restrict ourselves to type annotated (lambda )-terms, since our main interest is the development of a correct type checker for this language.
Previously published simulations have shown that the global and a posteriori CADM tests have a correct type I error rate and good power when applied to dissimilarity matrices computed from independently-generated raw data [ 50].
Simulation has shown that this method has a correct type I error rate and is more powerful in association detection, controlling for a strong confounder than logistic regression adjusting for the confounder (data not shown).
Taken together, the MLST results reveal a correct typing of the related strains.
Utilizing similar methods to those used in [13, 14], here we get a corrected type of the equivalent integral equation of (1.1), which is different from those gained in the existing literature.
If you do not have a memory card reader of the correct type, or a memory card slot of the correct type on your computer, you do not necessarily need to buy one.
However, accurate inference depends on a valid test (i.e., correct type I error rates), a requirement that may not be met if either familial relatedness or population structure is not properly modeled.
The proposed approach is useful as a complement to vision based classification, where road-users classes can be difficult due to partial occlusion or when the extraction of physical characteristics (descriptive features) of road-users does not provide enough details to reveal the correct type of a road-user.
