The phrase "associates a sample size" is correct and usable in written English.
It can be used in contexts related to research, statistics, or data analysis when discussing the relationship between a variable and the size of a sample.
Example: "The study associates a sample size of 100 participants with a higher accuracy in the results."
Alternatives: "links a sample size" or "correlates a sample size".
To analyze potential factors (14 variables of 4 types) associated with PCC, a sample size of 704 was required based on a previous study reporting that 12.0% of the population received PCC [ 22], with a limit of ±5% for the 95% confidence interval (CI; α = 0.05, d = 0.2 p).
The within-subject coefficients of variation associated with dihydroartemisinin (9) and piperaquine (10) AUCs were less than that associated with primaquine, so a sample size of 16 subjects also provided a satisfactory power for assessment of the effects of primaquine on dihydroartemisinin and piperaquine exposure.
The within-subject coefficients of variation associated with pyronaridine exposure (<12%) (27) and dihydroartemisinin exposure (<5%) (28) were less than that associated with primaquine; therefore, a sample size of 16 subjects also provided satisfactory power for all drugs.
To examine factors associated with CLBP in 2010 a sample size of 2842 was sought.
A subgroup analysis looking at the association between ENE and PLNM revealed that ENE was associated with PLNM in studies with a sample size larger than 100.
However, this is most probably due to a sample-size effect associated with p-values: when testing multiple hypotheses in a situation where the sample sizes differ with each test, the statistical power will generally be higher for tests run on larger samples.
However, as previously emphasized by several authors [ 32, 23], the use of weak instruments may lead to large standard errors in the IV estimates or even bias in the IV estimates if the weakness is associated with a small sample size or a violation of the main assumption.
The findings support the need to plan trials carefully as evidenced by reporting of methodological quality being associated with an a priori sample size calculation, valid methods of randomisation, and clear and complete reporting of the trial as recommended by CONSORT.
Employing Monte Carlo analysis, we examined potential errors associated with sample size in E, AS_stand, and JS using an original dataset with AS_b and Fd measured for 40 and 16 individuals, respectively.
The sampling design, performance criteria of the sampling and associated optimal sample size were obtained by statistical analysis of soil data available before the sampling.
