Statistical values

Read PCA eigenvalues, explained variance, loadings, scores, scree plots, signs, rotation, and communalities without universal loading cutoffs.

Read signs, magnitudes, uncertainty, missingness, and clusters in a correlation matrix while checking redundancy and multiple comparisons.

Distinguish eta squared from partial eta squared, read attributed effect-size conventions, and understand why their denominators prevent direct substitution.

Interpret Cohen’s d using attributed small, medium, and large conventions while keeping field context, uncertainty, and practical meaning visible.

Interpret standard deviation in the original units, compare spread responsibly, and know when skew, groups, or outliers make the value incomplete.

Distinguish an interval for a mean response from an interval for a new observation and understand why prediction intervals are wider.

Separate statistical evidence from practical magnitude using effect sizes, confidence intervals, and a context-defined minimum meaningful difference.

Read a z-score as position relative to a stated mean and standard deviation, then check the reference distribution before flagging extremes.

Distinguish linear Pearson correlation from rank-based Spearman and Kendall results, including ties, outliers, ordinal data, and nonlinearity.

Interpret Cronbach’s alpha, trace the disputed .70 cutoff, understand negative or very high alpha, and see why reliability is context-dependent.