The effective sizes for parametric tests, non parametric tests and chi square tests are as detailed below.
<h3>How to interpret the size of statistics test?</h3>
11) Parametric t-tests are statistical tests that assume the data approximately follows a normal distribution. Now, when trying to check for parametric t-tests, the effective size we use is Cohen's d which is an appropriate effect size for the comparison between two means.
12) Nonparametric t-tests are those statistical tests that don’t assume anything about the distribution followed by the data, and hence are also known as distribution free tests.
In non - parametric t - tests, we calculate effect size by using the formula;
r = z/√N
where;
r is effect size
z is z value
N is Observation number.
13) In the chi-square test, the effect size index (w) is calculated by dividing the chi-square value by the number of scores and taking the square root.
The effective size is considered small if w = 0.10, Considered medium if w = 0.30, and large if w = 0.50.
Read more about Statistics test size at; brainly.com/question/17203075
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