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Quantitative Methods: Reading #8, #9: Hypothesis Testing - parametric &…

- - - - Null Hypothesis (Ho)
        
        The hypothesis that the researcher wants to reject
        
        Is the hypothesis that is actually tested and is the basis for the selection of the test statistic
        
        typical statament:
        
        Ho: u = uo
        
        Ho: u < = uo
        
        Ho: u > = uo
      - Alternative Hypothesis (Ha)
        
        Want to be occurred
        
        Concluded if there is sufficient evidence to reject the null hypothesis
      - Determine One-Sided Test or Two-Sided Test
        
        One-Sided Test
        
        Upper (Right) tail: Ho: u < = uo versus Ha: u > uo
        
        Lower (Left) tail: Ho: u > = uo versus Ha: u < uo
        
        Two-Sided Test
        
        Ho: u = uo versus Ha: u # uo
        Reject Ho if: test statistic > upper critical value
        . . . . . . . . . . .test statistic < lower critical value
    - - Definition: A test statistic is a value calculated using a sample, when esed in conjunction with a decision rule, is the basis for deciding whether to reject the null hypothesis.
      - Test Statistic = ( Sample statistic - Hypothesized value ) / Standard erro of sample statistic
    - - There is always a possibility that the sample may not be perfectly representative of the population, resulting in the conclusions drawn from the test may be wrong, there are 2 types of errors that can be made when conducting a hypothesis test:
        
        Type I error: rejection of the null hypothesis when it is actually true (false decision).
        
        Type II error: failure to reject the null hypothesis when it is actually false (false decision).
        => We should choose appropriate Level of significance when testing
      - Decision
        
        Do not reject Ho
        
        True Condition: Ho is true = Correct decision
        
        True Condition: Ho is false = Incorrect decision (Type II error - B - Beta)
        
        Reject Ho
        
        True Condition: Ho is true = Incorrect decision & Type I error & Signigicance level a (alpha)
        
        True Condition: Ho is false = Correct decision & Power of a test = 1- B (beta)
    - - Critical Value
        
        A value compare to the test statistic to decide whether reject or not the Null hypothesis
        
        Identified by alpha and distribution probability table
      - A Confidence Interval
        
        A range of values within which the researchers believes the true population parameter may lie
        
        Confidence interval and a hypothesis test are linked by the critical value