G*Power is a fantastic tool for statistical power analysis.

To determine which statistical test to select in G*Power, consider the following factors:

1. Research question and hypothesis

2. Type of data (continuous, categorical, etc.)

3. Number of groups or conditions

4. Level of measurement (nominal, ordinal, interval/ratio)

Here are some common statistical tests in G*Power, grouped by category:

*Tests for Means*

1. t-test (two groups)

2. ANOVA (three or more groups)

3. ANCOVA (analysis of covariance)

4. Repeated measures ANOVA

*Tests for Proportions*

1. Chi-squared test (categorical data)

2. z-test (proportions)

*Tests for Correlations*

1. Pearson correlation

2. Partial correlation

*Tests for Regression*

1. Simple linear regression

2. Multiple linear regression

*Non-Parametric Tests*

1. Wilcoxon rank-sum test (two groups)

2. Friedman test (three or more groups)

To choose the correct test:

1. Identify your research design.

2. Determine the type of data you're working with.

3. Select the test that matches your research question.

If you provide more context or specifics about your study, I can offer a more tailored recommendation.

Salam

Thank you Kaija

The participants are selected from students in the university in which do not selected from statistical population and with random selection.

We used mental imagery (group 1), neurofeedback (group 2) and combined group neurofeedback+mental imagery (group3) training for improving mental toughness.Each of groups will participate in cross sectional 12 sessions. There are also some demographic variable that may consider as covariate. Data will analyses within pretest-post test design.

The data will be continuous and in interval/ratio level of measurement.

Allah Bless you.

each of ANOVA or MANOVA methods in Gpower suggest different sample size so we can use some statistical method that make the same results.