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Because biased vital rates can lead to inaccurate projections of the population growth rate (i.e., λ), there has been an upsurge in studies exploring alternative sampling designs for demographic studies [5] [7].
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Reviews in, say, newspaper books sections (I'm biased) are vital in offering a properly critical (often negative) opinion of new books: a necessary accompaniment to (also important) articles in the same sections that simply showcase books, or report interviews with authors: these can all too easily become elegant exercises in PR.
This bias has vital implications for efforts to expand Medicaid, which have largely been blocked by Republican politicians in states they control ever since the expansion was authorized by the Affordable Care Act in 2010.
Ability to discern these biases is vital to successful use of this approach and will help protect against validity threats to inferences made about data from the community.
(21, 22), Thus, although official mortality statistics have been collated and published since 1910 (20), the above events led to large biases in vital statistics data, and numerous barriers to producing reliable, representative and timely mortality statistics.
This is not because estimates of vital rates based on small sample sizes are biased.
When the number of individuals used to estimate vital rates is low, the resulting vital rates as well as estimates of variances and covariances among them can be biased.
As a consequence, database searches that are vital for the interpretation of mtDNA data can suffer from biased results when query and database haplotypes are annotated differently.
was biased.
Very biased.
To avoid this bias it is vital to employ a Tobit model rather than a traditional linear regression model.
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