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For each gene XRAY tests the probability of a 'false-positive' as a p-value >0.001.
As an example, for two tests the probability of observing both tests positive in the ith subpopulation is given as: <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0005221.e002.PNG" class= inline-graphic"/> The other three probabilities for the three test scenarios may be similarly derived.
However, in such multiple tests, the probability of false rejection of at least one null hypothesis can be high.
Since an abnormality, that is, suspicious cancer cell is confirmed only after a sequence of diagnostic tests, the probability of false positive is zero.
For three, four or five concurrent tests, the probability of at least one false positive is 14, 19, or 23%, respectively.
However, when thousands of genes in a microarray data set are evaluated simultaneously by fold changes and significance tests, the probability of detecting false positives rises sharply.
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The critical value depends on the significance level of the test (the probability of erroneously rejecting the null hypothesis).
A team based at the centers devised a mathematical model to test the probability of nearly every possibility -- ranging from a sick tourist returning from Tel Aviv to an infected goose to a mosquito confined eight or more hours in an airline cabin.
We also tested the probability of SHED in building an SEA model.
Very little empirical research has been done so far to test the probability of this theory with higher constraint designs.
In order to test the performance the spectrum sensing method, we usually test the probability of detection or the probability of misdetection for a fixed P f.
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