(This blog site does not support formatting therefore sometimes there could be errors in the post.)
A confidence interval is percentage of observations that are supposed to lie in that interval. e.g. 95% confidence interval is supposed to contain 95% of observations according to the specified criteria.
Confidence level is the confidence interval in which we expect to lie the given parameter of the hypothesis.
e.g. A hypothesis is rejected at 95% confidence level means that the given set of observations does not match with 95% of the population for the given parameter.
Significance Level, Critical Level
Significance level is the percentage of observations which lie beyond the desired confidence level. .
e.g. 95% confidence level means 5% significance level.
The critical value in an hypothesis test is the value of the parameter beyond which we would reject the null hypothesis.
Type I Error
Rejecting the null hypothesis when it is true .
Type II Error
Accepting the null hypothesis when it is false .
One sided tests or one tailed tests
A test in which we consider only one side of the distribution e.g . greater than and less than testing.
Two sided tests or two tailed tests
A test in which we consider only both sides of the distribution e.g. equal to and not equal to testing.
Two variables are associated if variation in one variable has effect on variation in other variables.