"sample confusion" is a correct and usable phrase in written English.
You could use it to refer to a feeling of being unsure what to do or say in a specific situation. For example, "After seeing the conflicting reports, I felt a lot of sample confusion."
Therefore, very strict lab routines need to be implemented in order to make sample confusion highly unlikely.
In fact, sample confusion involving segment 6 (Table 1 of [45]) could explain the simultaneous loss of those two mutations, without creating any further false mutation.
It is not helpful when clear instances of sample confusion or contamination – discovered through a posteriori analysis – get subsequently defended as "real" somatic effects by unconvincing arguments.
When numerous fragments of different genomes are PCR amplified and sequenced in a laboratory, there is a high immanent risk of sample confusion.
Furthermore, they may not remove all contaminating tissue, and can induce artificial cellular responses [ 25], while degrading samples and increasing the odds of sample confusion.
In order to minimize the risk of contamination or sample confusion going undetected it is advisable to use at least twice the number (32) of primer pairs for amplification that was employed by Tan et al. [ 12].
The equivocal evidence may, in part, be attributed to the use of different volumes of stretching prescribed between studies [ 7- 9], small sample sizes, confusion about the mechanism of action for explaining changes in hamstring extensibility, and the confounding effect of small mixed sex groups [ 10- 12].
To avoid the effects of small samples and confusion stemming from three more groups, subjects with other neurological diseases (OND, n = 24), clinically isolated syndrome (CIS, n = 20) and neuromyelitis optica (NMO, n = 5) were excluded, yielding 423 subjects: 155 healthy controls and 268 CDMS in the statistical analysis.
More importantly, if the number of test samples used in confusion matrices is small, the κ-statistic may not reflect the effect of many misclassified pixels (commission errors).
The estimation achieved a 94.45% degree of accuracy with associated AUC values between 0.997 and 0.955, with only two misclassified samples in the confusion matrix (specificity = 1, sensitivity = 0.909 using only the 7 genes validated by the qPCR).
To avoid confusion, the sampling index n is used for the high-rate signals while the low-rate signals utilize the index k.
