Error in sampling can be reduced by increasing the sample size.
Although it is challenging to collect data in forensic samples future studies should increase the statistical power by increasing the sample size and examine more subtle effects.
For example, if the aim is to detect change in abundance of the national badger population, then optimising precision by increasing the sample of social group size estimates may be necessary.
Random error arises as a result of variation between samples that might be drawn in a study and can be reduced by increasing the sample size.
However, the gain in accuracy by increasing the sample size declined rapidly when more than 16 measurements were taken to describe the gap shape.
Moreover, the SE could be further improved to 43 and 51 dB by increasing the sample thickness gradually to 51 and 73 μm, respectively.
These data suggest that collecting five or six repeated measurements may be sufficient for accurately assessing change and that attempts to further precision should be accomplished by increasing the sample size.
The methods are illustrated by their application to some well known examples and their performances are tested by some simulations, which show that the lack of uniqueness problem cannot be corrected for some experimental designs by increasing the sample size.
Secondly, the optimal approximate model of the impeller is constructed by using the Latin hypercube and radial basis function, and the fitting and optimization accuracy of the approximate model is improved by increasing the sample points.
Approximately results are consistent by increasing the sample size.
Weaknesses in data can be effectively addressed by increasing the sample size.
