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Supertyping simplifies data analysis and interpretation of results by reducing sampling error and strengthening statistical relationships.
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Applying different stock identification techniques on the same specimen reduces sampling error when interpreting results.
We investigate the use of Monte Carlo algorithms to improve the sampling of the weighted trajectories and thus reduce sampling error in a simulation of quantum dynamics.
Examples are given to show how increasing sample size reduces sampling error; increasing the fineness of grind and using larger subsamples reduces sample preparation error, and increasing the number of aliquots analyzed and using improved technology (HPLC versus TLC) decreases analytical error.
This approach also has added benefits in that it requires fewer parameter estimates, reduces sampling error, and decreases the likelihood of correlated residuals (Little et al. 2002) compared to item-level analyses.
This study investigates whether load estimates and resultant power to detect trends can be enhanced by (i) modelling the error structure so that temporal correlation can be better quantified, (ii) making use of predictive variables, and (iii) by identifying an efficient and feasible sampling strategy that may be used to reduce sampling error.
Other sampling approaches may reduce sampling error by design (e.g. stratification) or increase sampling error (e.g. cluster sampling).
To account for this variability, methods based on estimating parameters over "windows" have been successfully used to reduce sampling error while retaining true signal in studies aimed at identifying evidence of selection in populations [ 2- 5].
Extending the satellite data over 6 years (2001 2006) reduced sampling errors.
The well-defined sampling design and prior estimation of sample size reduces sampling errors.
We then address assembly-free issues that involve reducing sampling variation caused by incomplete coverage and sequencing errors.
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