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Many conventional statistical methods assume that all observations are independent and, hence, may produce misleading results if applied in this study and pseudo or false replication occurs when there is such a mismatch between the experimental design and the statistical analysis method (Hurlbert, 1984).
Unlike existing conventional statistical methods, most clustering algorithms do not rely on statistical distributions of data and thus can be helpful to apply when little prior knowledge exists on a certain issue [5].
Once that is done, conventional statistical methods can be used for further analysis.
Overall survival (OS) was analyzed using conventional statistical methods and the rank-preserving structural failure time (RPSFT) method to explore cross-over impact.
It can be used for solving problems that are not suitable for conventional statistical methods.
They can be compared to the results that would be obtained by conventional statistical methods.
The data were analysed by conventional statistical methods and by partial least squares discriminant analysis [79].
The obtained data were verified by conventional statistical methods.
This hypothesis can first be explored visually before further analysis using conventional statistical methods.
We further evaluated the prognostic/predictive impact of ER-β expression by conventional statistical methods.
This is likely to yield more consistent estimates than conventional statistical methods.
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