The main goals of power analysis are two folds in the process of designing an experiment, (a) how large a sample is required for MAKING statistical JUDGMENTS that are accurate and reliable and (b) how likely your statistical test will be to detect effects for a given size in a particular situation. 

In other WORDS, Power analysis is a very crucial ASPECT of experimental design. It helps us to determine the sample size required to detect an effect of a given size with a given degree of confidence. Conversely, it allows us to determine the probability of detecting an effect of a given size with a given level of confidence, under sample size constraints. If the probability is unacceptably low, we would be advised to alter or abandon the experiment.

The following four quantities are most important so far Power analysis is concerned:

1. sample size
2. effect size
3. significance level = P(Type I error) = probability of FINDING an effect that is not there
4. power = 1 - P(Type II error) = probability of finding an effect that is there

Given any three, we can determine the fourth.