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Analysis of Variance, a statistical method used to compare means of three or more samples to find out if at least one sample mean is significantly different from the others.

In a two-way ANOVA, the combined effect of two factors on the dependent variable that is different from the effects each factor would produce on its own.

The variance that is attributed to the differences between the mean of each group, indicating how much the group means deviate from the overall mean.

The average of the sum of squared deviations of the group means from the overall mean, an essential component in calculating the F-statistic.

The different conditions or values of a factor in an ANOVA.

The error made when a true null hypothesis is incorrectly rejected.

The error that occurs when a false null hypothesis is not rejected.

The conditions that must be met for the results of the ANOVA to be valid, including independence of observations, normality, and homogeneity of variances.

In the context of ANOVA, a factor is an independent variable that categorizes the groups being compared.

It represents the average variance within the groups, an essential component in calculating the F-statistic.

A ratio used in ANOVA to determine if between-group variability is significantly larger than within-group variability, calculated as the mean square between (MSB) divided by the mean square within (MSW).

Measurement of the variance within individual groups, reflecting how much the individual observations deviate from their respective group means.

A type of ANOVA that involves one independent variable with two or more levels to understand if there is a significant difference in the dependent variable.

The effect of an independent variable on the dependent variable averaged across the levels of any other independent variables.

The number of independent values or quantities which can be assigned to a statistical distribution. For ANOVA, DF is split into DF for between-group and within-group.

Additional tests conducted after an ANOVA when the null hypothesis is rejected to identify precisely which means are significantly different from each other.

The assumption in ANOVA that the variances within each of the groups are approximately equal.

ANOVA that involves two independent variables, examining both the individual and interactive effects on the dependent variable.

The hypothesis stating that there are no differences between the group means and any observed variation is due to random chance.