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anova

10 answered questions tagged “anova”. Browse and search all answers, or ask your own.

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Do robust standard errors fix floor and ceiling effects?

Robust standard errors repair the uncertainty around a group difference, not the difference itself. What that means when scores pile up at a scale limit.

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Is an F-test gate needed before Tukey's HSD?

Whether the overall F test must reject before follow-up tests between groups, what that gate does to false positives and power, and when F still helps.

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How many participants do you need when comparing several treatment arms?

Plan a multi-arm study around the comparisons you will report, not the ANOVA F test, and see how a multiplicity correction changes the numbers.

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Why does a t test use the t distribution and ANOVA the F distribution?

How the t, F and chi-square reference distributions are derived from the way each test statistic is built, and what to do when no formula exists.

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Significant ANOVA but no significant pairwise comparisons: how can that happen?

Why a one-way ANOVA F test can reject while every pairwise or Tukey comparison does not (and the reverse), and how far apart their p values can get.

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t tests vs the F test in regression: what is the difference?

Why regression output gives a t test for each coefficient and one overall F test, when the two agree exactly, and why they can point in opposite directions.

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Confidence interval for eta squared: does the Fisher z shortcut work?

Why the Fisher z shortcut gives unreliable intervals for eta squared, and how to get an exact one from F and its degrees of freedom.

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How to interpret the F statistic and p value in a one-way ANOVA

What the F ratio in an ANOVA table measures, how it maps onto the p value and the critical F, and what a significant F does and does not tell you.

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Planned contrasts vs post hoc tests: why do they give different p-values?

Why one comparison gets different p-values as a planned contrast, a Tukey test or a Bonferroni test, and when to use each.

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t test vs ANOVA with two groups: are the assumptions really different?

Why a pooled t test and a one-way ANOVA on two groups give the same p value, and why their normality assumptions are the same thing stated two ways.

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