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However, both sources of error can be reduced by careful attention to key aspects of study design.
The AutoPap system (Tripath Imaging inc, Burlington, NC, USA) addresses both sources of error.
If both sources of error are unbiased, this should tend to obscure clustering, reducing the probability of detecting clustering or of detecting differences among years or species.
Both sources of error can cause significant biases that can ultimately lead to wrong data interpretation, especially in those cases where a single gene is used for normalization.
In cases where the bias is obscured by statistical errors, using a combination of both extrapolation and the multilevel Monte Carlo method would be ideal, as it would reduce both sources of error.
We introduce a theoretical framework for analyzing both sources of error based on the notion of deviation from additivity, which quantifies the contribution of model misspecification to the estimation error.
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Comparing these to the mean of bootstrapped ^βx,j derived using fixed exposure model parameters (i.e., λ⊇= 0) gives us an approximation of the bias induced by the classical-like error (step 3), and the empirical SD approximates the SE that accounts for both sources of measurement error (step 4).
In Monodora, the latter would appear to be the case as phylogenetic resolution within the West species is low, and specimen locality data from two sister species pair in East Africa are few, both being sources of error.
Combining both sources of non-linkage error, the incidence proportion further attenuated to 18.5%w, an absolute difference of 9.8%.
Both of these sources of error, combined with different levels of systematic error in rate estimation on branches of different lengths, likely led to erroneous estimates of rate variation in datasets of variable branch lengths (Figure 4).
Data sets contain both random and systematic sources of error.
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