A significance test (also known as a hypothesis test) compares a sample with a population. Given what we know about the population, is it likely that the sample is representative of the population? Do we have evidence to show that the population parameter may not be accurate? These questions are just a few that statisticians address when performing significance tests.
There are four steps to performing a significance test:
1) State hypotheses (null and alternative) and the population parameter of interest.
Ho: mu = (population number)
Ha: mu >, <, or not = to (population number)
where mu represents.....(whatever your problem is about)
2) State assumptions. They are exactly the same as your confidence interval assumptions (random assumption, size assumption, and independent assumption).
3) Calculate your z-score using the z-score formula from earlier this year:
z = x-bar - mu/Sx/root(n), and calculate the p-value by using the Normalcdf function in your calculator.
4) Interpret in context for both your results and the context of the problem.
-Since the p-value (insert p-value) is (less than/greater than) the significance level (insert sig level here as a decimal), we reject/fail to reject the Null Hypothesis.
*Reject Null if less than, fail to reject Null if greater than
-We do/do not have sufficient evidence to show that (whatever your alternative hypothesis is saying in words, not symbols)
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