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1 biological research is systematic error, not random error.
2 e studies using strategies to limit bias and random error.
3 rostheses were substantially larger than the random error.
4 mortality rates in 2020 and allowing for 10% random error.
5 ive research to focus on both systematic and random error.
6 asurements, but is limited by systematic and random errors.
7 nt and individual-specific CNVs under normal random errors.
8 ules in the amplified product and introduces random errors.
9 es (so randomization is essential) and small random errors.
10 trategies that can counteract the effects of random errors.
11  rate determination, and much more robust to random errors.
12 ng competing explanations of data containing random errors.
13 icant impact on CV error (bias error: 0.65%, random error: 23.81%), while added noise had the most si
14 icant effect on CR error (bias error: 4.83%, random error: 6.38%).
15 f replicates, logarithmic transformation and random error analyses.
16 ived from NOESY spectra using MARDIGRAS with random error analysis.
17 d on: i), > or =20 restraints with up to 15% random error and no systematic error, or ii), > or =20 r
18  or spectral counts can highlight regions of random error and putative change.
19 ed scans) and reproducibility (including the random error and the instrument/operator variability) we
20 explored towards developing ways to minimize random errors and improve the precision of electrochemic
21 gn space; and on the challenges of measuring random errors and lab-related biases in measurement with
22 n this study, we address the problem of both random errors and systematic biases in microarray experi
23                                 Detection of random errors and systematic biases is a crucial step of
24 ise from a combination of systematic errors, random errors, and possible changes in solar structure.
25 [Formula: see text]) that takes into account random errors associated with atmospheric transport, atm
26 es a data model to adjust for systematic and random errors associated with different data sources.
27                  Subsequent reduction of the random errors based on multiple measurements over consec
28 fidence intervals reflect uncertainty due to random error but omit uncertainty due to biases, such as
29            Through the reduction of bias and random error by explicit, reproducible, comprehensive, a
30                            Here we show that random errors can be eliminated also by averaging mass s
31                                              Random errors can be reduced by averaging multiple measu
32 tain degree of effect underestimation due to random error cannot be ruled out.
33 cate that this diversity is not dominated by random errors generated during amplification.
34  interpreting medical data, exhibiting fewer random errors, higher accuracy, and better identificatio
35                             Here, sources of random error in MP enumeration in wastewater and other m
36 d the potential to yield false null results (random error in Na assessment, insufficient power).
37 of the relative importance of systematic vs. random error in science.
38 idence measure have included 1) ignoring the random error in T or 2) employing a Bonferroni adjustmen
39 example, the authors found that ignoring the random error in T provides a 95% CI for incidence as muc
40 to be observed and that these shifts are not random error in the measurement.
41 method was developed to allow correction for random error in the reference data when these data had d
42 s; (4) innovative approaches to characterize random error in the setting of constantly updated data;
43                                 Including no random error in the simulations, it was estimated that t
44 ly sampling from lognormal distributions for random error in the yearly public water district PFOA co
45 S-DPV) is proposed with the goal of reducing random errors in chemical- and bio-sensors and thereby i
46  premature responses, regressive errors, and random errors in males and perseverative errors in femal
47  in measurements and (2) recall noise due to random errors in measurements, using a bespoke statistic
48 suggest that systematic errors dominate over random errors in MRF scans under clinically relevant con
49  corrected these estimates for each survey's random errors in recorded birth month implied by differe
50 ors developed a simple statistical model for random errors in reported smoking (relative to true toba
51 erages gut microbial compositions to correct random errors in self-reported dietary assessments using
52           The method minimizes the impact of random errors in spectroscopic measurements by correctin
53  demonstrate its use to significantly reduce random errors in synthetic DNA.
54 latform significantly reduces systematic and random errors in the measurement by introducing two type
55 d, whereas rats with dorsal CA1 lesions made random errors in the process of completing the sequence
56 and-Altman plots revealed that the amount of random error increased as the magnitude of the change fr
57 a better understanding of the systematic and random error inherent in these coverage indicators can h
58             The first is the introduction of random errors into the genome by the viral polymerase, w
59 er, an automated system, while less prone to random errors introduced by human operators, may have sy
60 ishing between individual-level patterns and random error is challenging, highlighting the need for f
61 nomes, these suffer from the presence of 1-3 random errors/kb of DNA.
62 a bias less than 2.6 percent unit (p.u.) and random error less than 0.7 p.u. respectively.
63 e corrupted physical maps with an introduced random error of +/-6A are able to reconstruct the target
64  associated with fewest covariates and had a random error of 9.5 ms.
65                      Repeatability (based on random error of repeated scans) and reproducibility (inc
66 mass accuracies are normally limited only by random errors of low-abundance analytes, the method maxi
67                                              Random errors of the determinations relative to full sca
68                 The consequences of such non-random errors on association tests for rare variants are
69  for assessing the effects of systematic and random errors on the accuracy and precision of equilibri
70 nt CNV regions associated with either normal random errors or heavily contaminated errors.
71 rmance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by
72 metric, 'noise', that estimates the level of random errors present in each RPPA slide.
73   In addition, systematic bias potential and random error propagation are greatly reduced when CO(2)
74 to 0.14 mm for the Palmaz-Schatz stents; the random error (RE) was 0.03 to 0.14 mm.
75                                              Random error (SD inflation ratio) has less influence tha
76 d pressure may chiefly reflect the impact of random error, selective emphasis of particular results,
77 d distance restraints, including the number, random error, systematic error, distance distribution, a
78 ations, but each of these methods introduces random errors that are difficult to distinguish from gen
79 comparison, SDV was expensive and identified random errors that made little impact on results and cli
80 at divisions are surreptitiously recorded by random errors that occur during replication.
81  number of observations, while averaging out random errors, to predict the curvature at time zero, wh
82 , or ii), > or =20 restraints with up to 15% random error, up to 10% systematic error, and a symmetri
83  correct this omission, an estimator for the random error variance in this situation is developed bas
84 in systolic blood pressure was attributed to random error (visit-to-visit variability); average (cons
85 ch generates long (>16 kilobases) reads with random errors, we assembled 99% (244 megabases) of the O
86 y, we find that METRIC can still correct the random errors well even without including gut microbial
87                                        Small random errors were introduced to measured data to examin
88 xplained by unique environmental factors and random error, whereas shared environmental factors playe
89  biohazardous procedure, but also introduces random errors which contribute to variability in viral q
90  the overall evidence) and strict control of random error (which, in general, requires large numbers