# Baysian updating

It calculates the probability of an event in the long run of the experiment (i.e the experiment is repeated under the same conditions to obtain the outcome).Here, the sampling distributions of fixed size are taken.I’ve tried to explain the concepts in a simplistic manner with examples.Prior knowledge of basic probability & statistics is desirable.Similarly, intention to stop may change from fixed number of flips to total duration of flipping.In this case too, we are bound to get different depends heavily on the sample size.Then, the experiment is theoretically repeated infinite number of times but practically done with a stopping intention.

i.e If two persons work on the same data and have different stopping intention, they may get two different for the same data, which is undesirable.

Lets represent the happening of event B by shading it with red.

Now since B has happened, the part which now matters for A is the part shaded in blue which is interestingly .

Infact, generally it is the first school of thought that a person entering into the statistics world comes across.

Frequentist Statistics tests whether an event (hypothesis) occurs or not.

The objective is to estimate the fairness of the coin.