Upper bound for the expected number of false positives
This is a tool calculates the expected number of false positives according to the random k-set pooling design suggested by
Bruno et al. (1995)
Their analysis if based on random construction of the pooling matrix, wherein each column vector has the same weight.
The number of tests is the total number of pools and not the number of barcodes.
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Parameters:
Number of specimens (example: 1000):
Propability of mutant (example: 0.01):
Tests (example: 150):
Range of weights (example: 1-8):