Chapter 6

• Hypothesis Testing - used to determine whether a statement about the value of population parameter should or should not be rejected
• NULL Hypothesis - denoted by H0
• Alternative Hypothesis - denoted by Ha
• Null Hypothesis - tentative assumption about a population parameter
• Alternative Hypothesis - opposite of what is stated in the null hypothesis
• Type I Error - When you reject H0 (Null Hypothesis) when it is actually true
• The probability of making a type I error when the null hypothesis is true as an equality is called the level of significance
• Significance Tests


  Application of hypothesis testing that only control the Type I error
• Type II Error - When you accept H0 (Null Hypothesis) when it is actually false
• Statisticians avoid the risk of making a Type II error by using "do not reject H0" and "accept H0"
• P-Value - Probability computed using the test statistic that measures the support (or lack of support) provided by the sample for the null hypothesis
• If the p-value is less than the level of significance (a) - reject H0
• The test statistic z has a standard normal probability distribution
• If the p-value is greater than the level of significance (a) - fail to reject the null hypothesis
• If the absolute value of the t statistics is greater or equal to the T critical - reject
• If the absolute value of the T statistics is less than the T critical - fail to reject