One explanation could be that serum iron and NTBI are characterized by more random error (due to circadian fluctuations and day-to-day variations) than ferritin and transferrin, which may decrease the magnitude of its associations.
Despite data heterogeneity (historical data were available for between 1 and 65 readings), which may decrease the overall reliability of our findings, the approach substantially reduced the influence of measurement error compared with single measurement data.
"Blending breaks down fiber, which may decrease foods' satiety," explained Andy Bellatti, registered dietician.
Keypunching errors, which may occur during data entry, should be resolved before checking data errors.
However, large scale survey has limitations (e.g., nonsample errors including nonresponse, coverage errors and measurement errors), which may influence the accuracy of estimation.
This can result in significant statistical errors, which may translate into poorly supported phylogenomic supertrees.
Furthermore, bioinformatics algorithms could have inherent errors which may not be easily obvious to a biologist.
Single-point measurement increases the chance of random measurement error, which may underestimate the reported associations.
Dotted arrows in Figure 9 indicate imperfect conversion, in which the conversion inherently introduces errors, and may decrease the accuracy.
However, the variance of the residual error may decrease with increasing number of parameters.
