TEfficiency and Uncertainty about llp_sleptons_rpv_susy HOT 1 OPEN

Consulting "Wikipedia" as advised, this is the simplified error propagation formula:

Acceptance (A) = passed events/(passed+ non-passed events) = p/(p+n)
Uncertainty for p (passed) is square root p, for n (did not pass) is square root n

dA/dp = n/(p+n)^2

dA/dn = p/(p+n)^2

p = 7856     # Number of passed events 

n= 20000-p # Number of events that did not pass

p_err= math.sqrt(p)
n_err= math.sqrt(n)

dA_dp= n/((n+p)**2)
dA_dn= p/((n+p)**2)

sigma_A= math.sqrt((dA_dp*p_err)**2 + (dA_dn*n_err)**2 )

A= p/(p+n)
print("Acceptance is: ", A, "with uncertainty of  +/-", sigma_A)

And the output is:

Acceptance is: 0.3928 with uncertainty of +/- 0.0034533184040861334

Both methods agree to 2 significant-figure value of uncertainty = "0.0034"

from llp_sleptons_rpv_susy.