The phrase "allocation sample" is correct and usable in written English.
It can be used in contexts related to statistics, research, or resource management where a sample is designated for allocation purposes.
Example: "In our study, we will analyze the allocation sample to determine the distribution of resources among different groups."
Alternatives: "distribution sample" or "resource sample".
Data items obtained from the included articles were as follows: participants, interventions, comparisons, outcomes, study design and setting (PICOS), allocation, sample size, and possible bias.
In addition, study design related issues that introduce bias such as description of the randomisation procedure, concealment of allocation, sample size calculations or blinding have not been addressed frequently.
Biologists tend to use the conventional stratified estimator for an adaptive allocation sampling design in spite of its adaptive nature.
In this paper, we have introduced an adaptive allocation sampling design for which the conventional stratified estimator has some desired statistical properties.
The results show that the conventional stratified estimator for the introduced adaptive allocation sampling design is more efficient than other available estimators.
A structure is developed for the detection of epileptic seizure EEG signals based on sampling for the feature extraction stage by proposing optimum allocation sampling technique (OAT).
A stratified random digit dialing [ 22] with proportional allocation sampling methodology was adopted for this study.
The UNPBN method combines a nonparametric mixture model incorporating the Dirichlet process [ 21, 25] and an allocation sampler [ 26, 27].
Instead in Grzegorczyk et al. (2008), an allocation sampler is used in combination with Bayesian Networks to assign each observation to a group, but unlike changepoint models, this method treats the observations as being exchangeable, ignoring the fact that the data are sequential.
There are many different adaptive sampling designs including adaptive cluster sampling, two-phase stratified sampling, two-stage sequential sampling, and complete allocation stratified sampling.
Whenever there is a non-proportional allocation of samples, use of sample weights is an important step during analysis [ 12].
