Learning Objectives Describe the hypothesis testing process Distinguish the types of hypotheses Explain hypothesis testing errors Solve hypothesis testing problems – One population mean – One population proportion – One & two-tailed tests
Statistical Methods
What’s a Hypothesis? A belief about a population parameter – Parameter is population mean, proportion, variance – Must be stated before analysis I believe the mean GPA of this class is 3.5! © T/Maker Co.
Null Hypothesis What is tested Has serious outcome if incorrect decision made Always has equality sign: , or Designated H 0 – Pronounced ‘H sub-zero’ or ‘H oh’ Example – H 0 : 3
Alternative Hypothesis Opposite of null hypothesis Always has inequality sign: , , or Designated H 1 Example – H 1 : < 3
Basic Idea Sampling Distribution It is unlikely that we would get a sample mean of this value if in fact this were the population mean... therefore, we reject the hypothesis that = H0H0H0H0 H0H0H0H0
Level of Significance Defines unlikely values of sample statistic if null hypothesis is true – Called rejection region of sampling distribution Designated (alpha) – Typical values are.01,.05,.10 Selected by researcher at start
Rejection Region (One-Tail Test) Sampling Distribution 1 - Level of Confidence
Rejection Regions (Two-Tailed Test) Sampling Distribution 1 - Level of Confidence
Errors in Making Decision Type I error – Reject true null hypothesis – Has serious consequences – Probability of Type I error is Called level of significance Type II error – Do not reject false null hypothesis – Probability of Type II error is (Beta)
Decision Ho: Ha: Truth
Decision Results H 0 : Innocent
& Have an Inverse Relationship You can’t reduce both errors simultaneously!
H 0 Testing Steps Set up critical values Collect data Compute test statistic Make statistical decision Express decision n State H 0 n State H 1 Choose Choose n Choose n n Choose test
p-Value Probability of obtaining a test statistic more extreme ( or than actual sample value given H 0 is true Called observed level of significance – Smallest value of H 0 can be rejected Used to make rejection decision – If p-value , do not reject H 0 – If p-value < , reject H 0