STAT 100 Lecture Notes - Lecture 14: Normal Distribution, Type I And Type Ii Errors, Null Hypothesis

• School: University of Maryland
• Department: Statistics and Probability
• Course: STAT 100
• Professor: Cremins

Document Summary

Hypothesis testing about the mean when sigma is known (p-value method) Hypothesis testing is the other part of inferential statistics. The setup of our problem is that we want to determine whether a particular claim is believable, or not. This is one of the most common goals of statistics. A hypothesis test for a parameter is a procedure, based on sample evidence and probability, used to test a speci c claim about the value of the parameter. = population standard deviation p = population proportion. Since a claim can either be true or false, hypothesis testing is based on two types of hypotheses: The null hypothesis is a statement (regarding the value of a parameter) that is believed to be true: It is written as h(0) (read as h-naught or h-sub-oh ). It is a statement of status quo or no difference. Assumed to be plausible until we have evidence to the contrary.