difference between n and size parameters in np.random.binomial(n,p, size = 1000)

Question

I couldnt understand the difference between n and size parameters in np.random.binomial.

N = 1200
p =0.53
q = 1000

np.random.binomial(N, p, size = q) 
np.random.binomial(1, p, size = q)
np.random.binomial(N,p, size= q)

N is the number of trials, but is what is the size doing in the above formula. also kindly explain the vabove three versions of binomials.

Answer 1

1. np.random.binomial(N, p, size = q)
2. np.random.binomial(1, p, size = q)
3. np.random.binomial(N,p, size= q)

1st and 3rd are similar, i can see. These two are binomial random number generator

And, 2nd one is bernoulli random number generator

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Explanation of binomial:

A binomial random variable counts how often a particular event occurs in a fixed number of tries or trials.

Here,

• n = number of trials
• p = probability event of interest occurs on any one trial
• size = number of times you want to run this experiment

Suppose, You wanna check how many times you will get six if you roll dice 10 times. Here,

• n = 10,
• p = (1/6) # probability of getting six in each roll

But, You have to do this experiment multiple times.

Let, In 1st experiment, you get 3 six

In 2nd expwriment, you get 2 six

In 3rd experiment, you get 2 six

In Pth experiment, you get 2 six, here P is the size

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Explanation of bernoulli:

Suppose you perform an experiment with two possible outcomes: either success or failure. Success happens with probability p, while failure happens with probability 1-p. A random variable that takes value 1 in case of success and 0 in case of failure is called a Bernoulli random variable.

Here,

• n = 1, Because you need to check whether it is success or failure one time
• p = probability of success
• size = number of times you will check this

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You can also read this, numpy.random.binomial

Also, Difference between Binomial and Bernoulli