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One-way ANOVA, comparing multiple means

SS_between = Σ nᵢ (x̄ᵢ − x̄)²

Enter at least two groups of measurements and an alpha threshold: the tool calculates the one-way analysis of variance (between-group sum of squares, within-group sum of squares, total sum of squares, degrees of freedom, mean squares, F-statistic and p-value) then displays the complete ANOVA table and a conclusion that distinguishes absence of evidence from evidence of equal means.

Separate values with spaces, line breaks or semicolons. A comma is read as a thousands separator (1,234) when unambiguous, and rejected otherwise.

A value strictly between 0 and 1, for example 0.05.

Scientific dossier


What the tool computes, what it assumes, where it stops being valid, and where its data comes from.

Method & formulasSS_between = Σ nᵢ (x̄ᵢ − x̄)²

SS_between = Σ nᵢ (x̄ᵢ − x̄)²

SS_within = Σ Σ (xᵢⱼ − x̄ᵢ)²

SS_total = SS_between + SS_within

F = MS_between / MS_within

One-way ANOVA decomposes the total variability of the data into a component explained by group membership (between-groups) and a residual component (within-groups). The ratio of their mean squares, the F-statistic, departs from 1 when groups differ more from each other than within themselves.

k
· number of groups being compared.
N
· total sample size, all groups combined.
df between
· between-group degrees of freedom, k − 1.
df within
· within-group degrees of freedom, N − k.
MS
· mean square: sum of squares divided by its degrees of freedom.
P-value
· probability of observing an F-statistic at least as large if the population means were truly equal.
Alpha (α)
· significance level chosen before the test, typically 0.05: the p-value is compared to it to decide whether or not to reject the null hypothesis.
Validity domainOne-way ANOVA assumes independent groups, a distribution close to normal within each group, and comparable variances across groups.

One-way ANOVA assumes independent groups, a distribution close to normal within each group, and comparable variances across groups. With small sample sizes or very unequal variances, the p-value becomes less reliable; a Welch test or data transformation may then be preferable. Each group must contain at least two numerical values: with a single value, the within-group variance of that group is undefined.

Interpreting the resultA p-value below alpha signals a difference between group means too large to be attributed to chance: the equality hypothesis is rejected, but the test does not say which groups differ (a post-hoc test would be needed for that).

A p-value below alpha signals a difference between group means too large to be attributed to chance: the equality hypothesis is rejected, but the test does not say which groups differ (a post-hoc test would be needed for that). A p-value above or equal to alpha does not prove equal means: it only means that the data do not provide sufficient evidence against it, which may also reflect too small a sample size.