Statistical Significance signifies that result of a statistical experiment or testing is not occuring randomly and is attributable to certain cause. Statistical significance of a result could be strong or weak and it is very important for sectors which are heavily dependent on research works like insurance, pharma, finance, physics and so.
Statistical Significance helps in choosing the sample data so that one can judge the result or outcome of testing to be realistic and not be caused by a random cause.
Statisticians generally formulates the degree of statistical significance by sampling error. Generally sampling error of 5% is acceptable. Sample size is also important as it should be representative sample instead of very large sample considering the fact that large samples are prone to errors.
A level at which an event is considered to be statistical significant is termed as significance level. Statisticians uses a test statistic called p-value to get the statistical significance. If p-value of an event falls below a particular level then the event is considered as statistical significant. p-value is function of standard deviations and means of data samples. p-value is the probability of an event which certifies that result of statistical testing is occuring by chance or due to some sampling error. In other words it is the risk of failure of a statistical test. Opposite of p-value is confidence level which is 1 - p-value.
If p-value of a result is 5% then that means confidence level of the result is 95%.