Analysis of variance is a powerful tool for comparing means across three or more groups, but the F-test only behaves the way the theory says it should when certain conditions hold. Three assumptions underlie the standard one-way ANOVA: the dependent variable is approximately normally distributed within each group, the population variances are equal across groups (homogeneity of variance, or homoscedasticity), and the observations are independent of one another. Independence is largely a matter of how the data were collected; normality and equal variance can be checked after the fact with plots and formal tests.
This matters in an engineering context because you will routinely compare several process settings, materials, suppliers, or operators, and the conclusion you report depends on whether the F-statistic is trustworthy. ANOVA is fairly robust to moderate departures from normality, especially with balanced and reasonably large samples, but it is much less forgiving of unequal variances combined with unequal group sizes, and it is not robust at all to violations of independence. Knowing this tells you when to proceed, when to transform the data, and when to switch to an alternative such as Welch's ANOVA or the Kruskal-Wallis test.
The videos below approach assumption checking from three angles: the underlying logic and hand calculation of a variance-equality test, a software walkthrough in SPSS, and a combined normality and equal-variance check in R. As you watch, pay attention to what the null hypothesis of each assumption test actually says, what a small p-value for an assumption test implies about your ANOVA, and how the presenter decides whether a violation is serious enough to require a remedy. Also note the difference between a visual diagnostic (histograms, Q-Q plots, residual plots) and a formal test such as Levene's, Bartlett's, or Shapiro-Wilk.
This lecture focuses on testing the assumption of equal variances in the ANOVA setting, opening with the point that the F-ratio is relatively insensitive to modest departures from that assumption. Expect a discussion of why the equal-variance condition is built into the ANOVA model and a worked procedure for formally testing whether group variances can be treated as equal. The treatment is conceptual and computational rather than software-based.
It directly addresses the homogeneity of variance subtopic and, importantly, frames the issue in terms of robustness, which is exactly the judgment call students need to make about the consequences of violating assumptions.
A short, focused SPSS demonstration showing how to test homogeneity of variances in the context of a mixed ANOVA design, beginning with an explanation of what the assumption means before moving into the software steps. The video should show where the relevant output appears and how to read it. Its brevity makes it a quick reference rather than a full lecture.
Statorials produces concise, step-by-step statistical software tutorials, and this clip connects the abstract assumption to concrete output you would actually inspect before trusting an ANOVA result.
This tutorial covers both assumption checks for a one-way ANOVA in R: normality of the dependent variable within groups and equality of variances across groups. The description states the assumptions explicitly, so expect the relevant R functions and interpretation of their output. It bundles the two testable assumptions into a single short workflow.
It is the most complete fit for the "verifying assumptions before conducting ANOVA" subtopic because it treats normality and homoscedasticity together as a pre-analysis routine, and it does so in a reproducible scripting environment.
These three videos support MO2 most directly: between them they cover the logic of the equal-variance assumption, a formal test computed by hand, and software-based verification of both normality and homogeneity in SPSS and R, which is precisely the "visual methods and formal statistical tests" skill the outcome calls for. They also underpin MO3 and MO5 indirectly, since an F-statistic and any post-hoc comparisons are only interpretable when the underlying assumptions have been checked and any violations addressed. Use the remaining subtopics in the module notes to fill in independence of observations and the alternatives such as Welch's ANOVA, Kruskal-Wallis, and variance-stabilizing transformations, which the videos touch on only lightly.
These videos were chosen from YouTube on October 2, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.