In probability, you learn about how to compute the probability of an event given the model (e.g. distributions

of the random variables involved in that event). For example, questions such as "what is the probability that

you will get at least 4 heads when you toss a coin 25 times. Here, the distribution is the binomial one, and given the

distribution, you can compute the required probability.

In statistics, you are given the data, and are being asked to compute the model.

A
general prediction problem may start with observed data, fit a model to
it, and use the model to compute the probability of some event.

In
the coin example above, suppose the coin can be a biased one. You are
given the outcomes of 200 coin-toss experiments with that

coin, and are then asked to predict the probability that in the next 25 coin tosses, it will come up heads at least 4 times.

In
this case, you will first learn the "parameters" (i.e, p--the
probability that the coin comes head) from the data. This is
statistics.

Then, given p, you plug it into the binomial
distribution and can compute the probability that the coin will come
heads 4 times at least in the

next 25 tosses.

Does this help?

Rao

**Srinath Bodala**< sbodala@asu.edu> wrote:

Hi,You have made the statement that "Probability is inferring from the model of the world and Statistics is creating model from data". Defining statistics as creating model from data is okay but I found it difficult to understand how probability is about inferring from the model of the world. Have you made this statement in general or specifically to say that you can caluculate the probabilty of different things once you know the joint probability.If you have made it in general we can say that anything we do is an inference thinking in very abstract,even statistics.Thanks,Srianth Reddy B.