I am struggling to understand this:

first I have read the theory of discrete and continues random variable, and distributions. where an outcome is the random variable that behaves as a certain distribution.

but I came across with a situation where I cannot compare it with the previous concept( maybe it is my wrong interpretation ). the situation is as follow:

a component insulator strength is tested by applying a voltage (shots). this voltage is increased so the result (faults) is as followed:

[V ]------- [# "shots"]-- [# faults]-- [%faults/shots]

[900] ------ [100 ]--- [ 2 ]--- [ 0.02]

[1000 ] ---- [ 40 ] --- [ 20]--- [ 50]

[1050 ]---- [ 40]--- [ 33 ] --- [ 82.5]

[1075 ]---- [ 100 ] --- [ 93] --- [ 93]

[960 ] --- - [ 40 ]--- [ 7 ] --- [ 17.5]

[980 ] --- [ 40 ]--- [ 16] --- [ 40]

[960] --- -[ 40]---- [ 10]--- [ 25]

so my interpretation is: each test is a random process with N trials( #shots) and S outcomes(#faults) so for each sample a have a probability P(test1) = 0.02,...and so on, then I am blocked..............., how can analyze this data when the sum of probabilities is not "1" the histogram shows a rising curve instead of bell curve? or it is actually a CDF(cumulative distribution function?)

first I have read the theory of discrete and continues random variable, and distributions. where an outcome is the random variable that behaves as a certain distribution.

but I came across with a situation where I cannot compare it with the previous concept( maybe it is my wrong interpretation ). the situation is as follow:

a component insulator strength is tested by applying a voltage (shots). this voltage is increased so the result (faults) is as followed:

[V ]------- [# "shots"]-- [# faults]-- [%faults/shots]

[900] ------ [100 ]--- [ 2 ]--- [ 0.02]

[1000 ] ---- [ 40 ] --- [ 20]--- [ 50]

[1050 ]---- [ 40]--- [ 33 ] --- [ 82.5]

[1075 ]---- [ 100 ] --- [ 93] --- [ 93]

[960 ] --- - [ 40 ]--- [ 7 ] --- [ 17.5]

[980 ] --- [ 40 ]--- [ 16] --- [ 40]

[960] --- -[ 40]---- [ 10]--- [ 25]

so my interpretation is: each test is a random process with N trials( #shots) and S outcomes(#faults) so for each sample a have a probability P(test1) = 0.02,...and so on, then I am blocked..............., how can analyze this data when the sum of probabilities is not "1" the histogram shows a rising curve instead of bell curve? or it is actually a CDF(cumulative distribution function?)

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