Sentence examples for missing data often from inspiring English sources

Although genotyping microarrays produce some missing data, GBS produces an extremely sparse genotype matrix with mostly missing data: often hundreds of thousands of SNPs are discovered, but only a small fraction of the SNPs (e.g., <10%) pass missing data thresholds (Gardner et al. 2014).

Yet the theoretical foundations of data models are not designed to support references to missing data (often termed nulls).

When confronted with missing data, often it is reasonable to assume that the mechanism underlying missingness is related to observed but not to unobserved outcomes (missing at random, MAR).

As this data increases, management is perhaps the biggest problem to address within this paradigm of big data as missing data often occurs and is harder to validate, given the volume of information (Kaisler et al. 2013).

Given that we find that excluding genes with missing data often decreases accuracy relative to including these genes (and that decreases are generally of greater magnitude than increases), there is little basis for assuming that excluding these genes is necessarily the safer or more conservative approach.

The general issue of missing data often represents a substantial consideration for analysis of pesticide exposures.

Concerns about the deleterious effects of missing data may often determine which characters and taxa are included in phylogenetic analyses.

However, sampling the missing data is often time consuming and the algorithm is also slow to converge.

9 10 Assumptions about the missing data can often be supported by collecting and reporting suitable information.

41 In these situations, missing data may often be regarded as ignorable.

Participants with missing data are often a non-random subset of the sample, increasing the risk of biased estimates of treatment effects.