Hi;

My G*Power setup is as follows and my method is A.R.E.

1) What does ARE stand for in G*Power?

2) my degree of freedom is 53.2960000. My question is that is my DF Ok? I didn't set it up specifically and the value is awkward.

3) how can i interpret this with my results? E.g. Given a b and c, the mean difference is k, so that I can reject/accept null hypothesis. What are my parameters?

Best regards.

My G*Power setup is as follows and my method is A.R.E.

1) What does ARE stand for in G*Power?

2) my degree of freedom is 53.2960000. My question is that is my DF Ok? I didn't set it up specifically and the value is awkward.

3) how can i interpret this with my results? E.g. Given a b and c, the mean difference is k, so that I can reject/accept null hypothesis. What are my parameters?

```
t tests - Means: Wilcoxon-Mann-Whitney test (two groups)
Options: A.R.E. method
Analysis: A priori: Compute required sample size
Input: Tail(s) = One
Parent distribution = min ARE
Effect size d = 0.9076923
α err prob = 0.05
Power (1-β err prob) = 0.95
Allocation ratio N2/N1 = 1
Output: Noncentrality parameter δ = 3.3748581
Critical t = 1.6739508
Df = 53.2960000
Sample size group 1 = 32
Sample size group 2 = 32
Total sample size = 64
Actual power = 0.9541689
```

Best regards.

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ARE~(from the GPower documentation)That is when using the Wilcoxon test you are no worse off than 0.86 times the value from the t-test, and in cases where the data is badly not normal you are usually much better off (ARE > 1).

Here the GPower is looking at the distribution of H which is the difference in distributions of F (your base situation) and G (your testing case), where G is F shifted, that is G(x) = F(x − D) and D is the shift amount. The ARE gives details as sample size increases to infinity.

2) Degrees of freedom typically describe how much

movementyou have in the data for estimation. If degrees of freedom is zero then there is none, and the answers will be fixed and completely determined. In statistics df=0 doesn't make sence. In simple cases the df can be calculated and ends up a nice integer number, however in cases where approximations are used for distributions, or more advanced techniques are used etc, a formulae that gives an expression for df usually has fractions. This means that there is not really an exact expression, some approximations (about distributions etc) have been made and the df value that is needed for looking up tables etc is no longer just an integer. A conservative procedure is to round down.3) Interpretation

(reporting a difference found when it was just due to sampling variations)of 5%.(Not missing out an actual difference due to sampling variations)to be at least 95% (This is usually considered high if it involves real money in collection data - often 80% is regarded as agoodvalue to use).For those settings -

Ian