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Hypothesis Testing in Statistics: Understanding Null & Alternative Hypotheses

Learn about NULL and ALTERNATIVE HYPOTHESES in statistics, how to set them up, and conduct hypothesis testing. Examples and procedures included. Access class slides online, and understand statistical tests and significance levels.

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Hypothesis Testing in Statistics: Understanding Null & Alternative Hypotheses

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  1. MKT 317 Ryan White N462 whiter@bus.msu.edu Office Hours: Tuesday 9:00 – 10:00 am Thursday 9:00 – 10:00 am & By Appointment

  2. About Recitation • Class Format • Class Slides • NOT ON ANGEL http://www.msu.edu/~whitery2/mkt317slides.html

  3. Two Types of Hypotheses NULL HYPOTHESIS Statement of no effect, no relationship, or no difference among the variables (independence) – denoted H0 ALTERNATIVE HYPOTHESIS Statement in which some effect, relationship, or difference is expected (dependence) – denoted H1

  4. *ALWAYS * Put the equality in the NULL hypothesis

  5. Put claim in specific form • If it has an equality in it → it is the null → alternative is the opposite without an equality • If it does not have an equality in it → it is the alternative → null is the opposite with an equality

  6. Example 1 Mark Dantonio claims that his football team scores 49 points per game. How would you set up the null and alternative hypothesis to test his claim?

  7. Hypotheses H0: µ = 49 points H1: µ ≠ 49 points

  8. EXAMPLE 2 Government Agencies are devoted to ensuring that food producers package their products such that the volume listed on labels is correct. For example, bottles of ketchup labeled 16 oz must contain at least 16 oz. It is impossible to check all packages, so sampling is used in order to check whether there is enough evidence to infer with a 5% significance level that the mean weight of all bottles is under 16 oz.

  9. Hypotheses H0: µ  16 ounces H1: µ < 16 ounces

  10. Hypothesis Testing Procedure • Specify the null and alternative hypotheses • Choose the appropriate statistical test • Specify the desired level of significance • Compute the value of the test statistic • Compare the test statistic to the critical value (obtained in step 3)

  11. HOW TO READ A Z-TABLE

  12. HOW TO READ A T-TABLE

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