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Hypothesis Testing - Coggle Diagram

- - - - z-score is a test statistic which means that the sample data are converted into a single, specific statistic that is used to test a hypothesis
- - - - two-tailed tests used when there is no strong directional expectation or when there are two competing predictions
        
        one-tailed tests used when the directional prediction is made before the research is conducted and there is a strong justification for the directional prediction
- - - - if the prediction is that treatment will produce a decrease in scores, the critical region is located almost entirely in the left handed tail of the distribution
        
        have to find the critical region value in the form of a z-score to know where it falls in the distribution
- - - - M Critical : marks where results become statistically significant
        
        M Critical = µnull + z(standard error)
    - - 1) z-score must be calculated for M Critical
        2) the proportion is found in the unit normal table
        
        z = (M Critical - µalternative)/standard error
    - - when sample size is small, statistical power will be low and when power is low, it is less likely that the null hypothesis will be rejected
- - - - Cohen's d recommends that the mean difference can be standardized by measuring the mean difference in terms of the standard deviation
        
        Cohen's d = mean difference/standard deviation
        
        measures the distance between two means and is typically reported as a positive number