Briefly and concisely discuss in your own words the followin

Briefly and concisely discuss in your own words the following:
a. Compare the t statistic with the zscore. Include in your comparison the
reason why you would choose one over the other. (Give at least 2 reasons.

b. Compare Cohen’s d and 2. When and why are they used?

c. Compare the independentmeasures t test with the repeated measures t test.
Give an example of each

d. Explain how to determine the Fcrit value using Table B.4, The F Distribution,
on pages 705–707 of your textbook.

e. Define Fratio and explain its function in ANOVA.

f. Compare the mean squares between treatments with the mean squares within
the treatment.

Solution

a. z-scores are a conversion of individual scores into a standard form. The conversion is based on your knowledge about the population’s standard deviation and mean. A z-score tells you how many standard deviations from the mean your result is.  t-scores are also a conversion of individual scores into a standard form. However, t-scores are used when the conversion is made without knowledge of the population standard deviation and mean. A z-score and a t score are both used in hypothesis testing. The general rule of thumb for when to use a t score is when your sample size meets the following two requirements:

In other words, you must know the standard deviation of the population and your sample size must be above 30 in order for you to be able to use the z-score. Otherwise, use the t-score.

b) Cohen\'s d is a standardized index of a mean difference (e.g. between two samples or between two measurement points). The mean difference is standardized (i.e. divided) by the standard deviation. d = 0.5 means that the mean difference is half a standard deviation. The partial eta-squared is a variance proportion in an ANOVA. It is computed as systematic variance of a factor / (systematic variance of a factor + error variance). Partial eta-squared = 0.5 would mean that the systematic variance related to a factor is as high as the error variance in your ANOVA.