Key Concepts
- If the alternative hypothesis contains > or <, then the test is called a one-sided test. If the alternative hypothesis contains ≠, then the test is called a two-sided test.
- The basic steps to doing a hypothesis test are:
- Determine what procedure should be done.
- State the null and alternative hypotheses.
- Check that the conditions for doing the procedure have been met.
- Calculate the test statistic and p-value.
- Make a conclusion based on an [latex]\alpha[/latex] level.
- State your conclusion in the context of the problem.
Glossary
Hypothesis: a statement about the value of a population parameter. In case of two hypotheses, the statement assumed to be true is called the null hypothesis (notation [latex]H_0[/latex]) and the contradictory statement is called the alternative hypothesis (notation [latex]H_a[/latex]).
Hypothesis Testing: based on sample evidence, a procedure for determining whether the hypothesis stated is a reasonable statement and should not be rejected, or is unreasonable and should be rejected.
Level of Significance of the Test: probability of a Type I error (reject the null hypothesis when it is true). Notation: [latex]\alpha[/latex]. In hypothesis testing, the Level of Significance is called the preconceived [latex]\alpha[/latex] or the preset [latex]\alpha[/latex].
p-value: the probability that an event will happen purely by chance assuming the null hypothesis is true. The smaller the p-value, the stronger the evidence is against the null hypothesis.
Candela Citations
- Provided by: Lumen Learning. License: CC BY: Attribution
- Introductory Statistics. Authored by: Barbara Illowsky, Susan Dean. Provided by: OpenStax. Located at: https://openstax.org/books/introductory-statistics/pages/9-key-terms. License: CC BY: Attribution. License Terms: Access for free at https://openstax.org/books/introductory-statistics/pages/1-introduction