The parametric statistic used to determine whether statistically significant differences occur when comparing more than two mean scores is referred to as:

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The question asks about the parametric statistic used to determine whether statistically significant differences occur when comparing more than two mean scores.
Option 1: 'Student t-test' – The t-test is typically used for comparing the means of two groups. When there are more than two groups, using multiple t-tests increases the risk of Type I error, so this is not the standard method for comparing three or more means.
Option 2: 'Fisher test' – This likely refers to Fisher's exact test, which is used for examining associations in small sample categorical data, not for comparing means across multiple groups. It does not address mean differences among more than two groups.
Option 3: 'Chi-square test' – The chi-square test assesses associations between categorical variables or goodness-of-fit, not differences between means of continuous data across groups. It is not the method for comparing means.
Option 4: 'Analysis of Variance (ANOVA)' – ANOVA is specifically designed to test whether there are statistically significant differences between the means of three or more independent groups. It partitions variance into between-group and within-group components and uses the F-statistic to determine significance, making it the standard parametric approach for this purpose.
In summary, among the listed options, ANOVA best fits the scenario of comparing more than two mean scores, whereas the other tests are intended for two-group comparisons, categorical data, or non-mean-based analyses.

The parametric statistic used to determine whether statistically significant differences occur when comparing more than two mean scores is referred to as: Single choice

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