Paste independent group data, calculate between/within sums of squares, F, p, eta-squared, and omega-squared, and review the assumptions before interpreting the result.
| Source | SS | df | MS | F | p |
|---|
Add per-group n/mean/SD, degrees of freedom and sample-balance evidence to the ANOVA result.
| Group | n | Mean | SD |
|---|
Capture the current inputs, analysis settings and visible result as stable fingerprints, then flag structural method-fit issues that can be checked from the entered data. This complements the page’s existing mathematical audit; it does not prove distributional assumptions or causal validity.
Use descriptive context first, choose the method that matches the design, then keep inputs, settings and result evidence reproducible instead of copying a p-value without its analysis recipe.
Keep the existing ANOVA table above, then review group balance, spread, effect size, and the decision threshold before deciding whether a classical equal-variance ANOVA fits the study.
Uses Group A, B and C raw values above.
A significant omnibus test still needs a suitable post-hoc analysis for pairwise claims.
| Group | n | Mean | SD | Variance |
|---|
Refresh after editing the three groups above.
Use plots and descriptive summaries before formal tests. Check the sampling/design assumptions that matter for the chosen method, report effect size and uncertainty where available, and avoid treating a threshold such as p < .05 as proof of importance, causation, or truth. Browser calculations are educational/planning utilities, not domain-specific professional advice.
Use the product workflow above first. These notes add interpretation, verification and limits without displacing the inputs, results or visual review.
A significant one-way ANOVA says the group means are not all equal; it does not identify which specific groups differ. Use a suitable post-hoc procedure with multiplicity control when the research question requires pairwise conclusions.
Compare sample sizes, means, standard deviations and the largest-to-smallest variance. Very unequal spreads or strongly unbalanced groups can make the classical equal-variance ANOVA a poor fit, especially when the largest variance occurs in the smallest group.
Repeated measurements from the same person, clustered observations or paired data are not independent simply because they were pasted into separate boxes. The sampling and study design determine independence, so choose a repeated-measures or clustered method when appropriate.
Eta-squared and omega-squared describe the share of outcome variation associated with group membership under this model. A very small p-value can accompany a trivial practical effect in a large sample, so report an effect size and domain context alongside significance.
Changing α from .05 to .01 changes the threshold for a decision but does not change the observed F statistic or p-value. Report the chosen alpha, F degrees of freedom, p-value and effect size so another reader can reproduce the interpretation.
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