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As mentioned above, the small survey sample may reduce generalizability of the results.
First, the small survey sample may reduce the generalizability of results.
Furthermore, the use of a non-clinical sample may reduce some of the selection bias associated with clinical populations.
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Rapid multiplex real-time PCR tests to be applied directly to clinical samples may reduce the time to diagnosis [ 81].
In many cases one sample will not provide coverage of differentially expressed genes for the whole series, whereas pooling of samples may reduce fluctuations.
Other sampling approaches may reduce sampling error by design (e.g. stratification) or increase sampling error (e.g. cluster sampling).
Results of the case study, optimization of carbon dioxide capture process with aqueous amines, revealed that the mixed adaptive sampling algorithm may reduce the required sample size by up to 40% compared to a purely space-filling design.
A smaller sample chamber may reduce the response times.
Decreasing the number of sample plots may reduce only our possibility to map variability in the study area and limit number or range of homogenous patches.
Less studies numbers and small sample size may reduce the statistical power of analysis.
Third, the relatively small sample size may reduce the power to explore subgroups analysis.
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