Answer / manojkumar

Hi Siva Reddy,
Your answer is little bit tuf for me to understand.
So i gone through google and i found the following answer
for my question.
Just take a look over that.

Story point is a sub part of Agile methodology. Here I am
explaining how agile works.
For agile, the team (including customers, product manager,
project manager, developers and testers) works in terms of
sprint. Sprint is nothing but a full SDLC, for limited time
period (in general for 3-4 weeks)
Agile methodology is useful while no requirements or rapid
changes in requirements for the project.
For one sprint, the scrum master (generally the product
manager) decides the tasks to be achieved.
For each task, separate story being created.
For each story, story points created (just to break the
story in smaller parts) and then whole team estimates for
particular story point and then the story point being
freeze.
For Agile, each and every team member is involved in
estimating (in terms of time to achieve the task)
particular task / story point.

Answer / siva reddy

When a parameter is being estimated, the estimate can be
either a single number or it can be a range of scores. When
the estimate is a single number, the estimate is called a
"point estimate"; when the estimate is a range of scores,
the estimate is called an interval estimate. Confidence
intervals are used for interval estimates.

As an example of a point estimate, assume you wanted to
estimate the mean time it takes 12-year-olds to run 100
yards. The mean running time of a random sample of
12-year-olds would be an estimate of the mean running time
for all 12-year-olds. Thus, the sample mean, M, would be a
point estimate of the population mean, μ.

Often point estimates are used as parts of other statistical
calculations. For example, a point estimate of the standard
deviation is used in the calculation of a confidence
interval for μ. Point estimates of parameters are often used
in the formulas for significance testing.

Point estimates are not usually as informative as confidence
intervals. Their importance lies in the fact that many
statistical formulas are based on them.

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