Sentence examples for might be erroneous from inspiring English sources

It does not know how many of these might be erroneous.

Conclusions drawn from hypothesis testing results might be erroneous if effect sizes are not judged in addition to statistical significance.

While this is useful for most purposes and allows us to respond quickly and appropriately, this also makes the brain jump to conclusions that might be erroneous (Fig. 2).

"The US Supreme Court has made clear that identification evidence in particular should not be suppressed from trial simply because it might be erroneous," Feldman said.

Given the similarity between both sequences it might appear at first sight as though the novel peptide might be erroneous.

Due to the fact that the results – and consequently our interpretations – of the docking procedure might be erroneous we performed an additional flexible alignment of compounds 1, 4, 5, and 6, and calculated a consensus pharmacophore model (Figure 3B).

We must not forget that it might often be erroneous to conclude that processes that produce same (or similar) results are same; merely because the results from them are matching (in some significant confidence interval).

Since 70-80% of processed food products contain GM ingredients, consumers would see a lot of labels in a supermarket--studies show they couldn't explain what GMOs are, or worse, the explanations they might give would be erroneous.

Unmentioned in the hype surrounding the Harvard and Virginia research was the uncomfortable possibility to which this points: that the very idea of a fixed and stable self might be, somehow, erroneous.

In a statement the medical association warns, "The A.M.A. is greatly concerned that a substantial proportion of information on the Internet might be inaccurate, erroneous, misleading or fraudulent and thereby pose a threat to public health".

Moreover, if a dataset has a significant number of recently evolved sequences, the conservation score of the alignment columns picked up as conserved might be an erroneous representation of the data set.