when anova assumptions are violated

How do you know if an assumption is violated?

Potential assumption violations include:Implicit factors: lack of independence within a sample.Outliers: apparent nonnormality by a few data points.Patterns in plot of data: detecting nonnormality graphically.Special problems with small sample sizes.Special problems with very large sample sizes.

What does it mean when assumptions are violated?

a situation in which the theoretical assumptions associated with a particular statistical or experimental procedure are not fulfilled.

What are the 3 main assumptions of ANOVA?

Assumptions for One-Way ANOVA Test There are three primary assumptions in ANOVA: The responses for each factor level have a normal population distribution. These distributions have the same variance. The data are independent.

What do you do if t-test assumptions are violated?

When t test assumptions are violatedCheck the data – in particular, make sure that that the problematic data are true outliers and not errors in copying.Ignore the problem – not recommended since this will often yield inaccurate results, although often acceptable if the violation of the assumptions is not too severe.More items...

What would be the possible consequence of violating the assumption underlying the ANOVA?

If the assumption of normality is violated, or outliers are present, then the one-way ANOVA may not be the most powerful test available, and this could mean the difference between detecting a true difference among the population means or not.

How do you know if ANOVA assumptions are met?

To check this assumption, we can use two approaches: Check the assumption visually using histograms or Q-Q plots. Check the assumption using formal statistical tests like Shapiro-Wilk, Kolmogorov-Smironov, Jarque-Barre, or D'Agostino-Pearson.

Why are ANOVA assumptions important?

Data Level and Assumptions Like the t-test, ANOVA is also a parametric test and has some assumptions. ANOVA assumes that the data is normally distributed. The ANOVA also assumes homogeneity of variance, which means that the variance among the groups should be approximately equal.

When repeated measures are used which assumption is violated?

Unfortunately, repeated measures ANOVAs are particularly susceptible to violating the assumption of sphericity, which causes the test to become too liberal (i.e., leads to an increase in the Type I error rate; that is, the likelihood of detecting a statistically significant result when there isn't one).

What if assumptions of multiple regression are violated?

If the assumption of normality is violated, or outliers are present, then the multiple linear regression goodness of fit test may not be the most powerful or informative test available, and this could mean the difference between detecting a linear fit or not.

What does it mean when the assumption of homogeneity of variance is violated?

If group sizes are vastly unequal and homogeneity of variance is violated, then the F statistic will be biased when large sample variances are associated with small group sizes. When this occurs, the significance level will be underestimated, which can cause the null hypothesis to be falsely rejected.

How do you know if a homoscedasticity assumption is violated?

A scatterplot in a busted homoscedasticity assumption would show a pattern to the data points. If you happen to see a funnel shape to your scatter plot this would indicate a busted assumption. Once again transformations are your best friends to correct a busted homoscedasticity assumption.

When should we be concerned or not too concerned about violating the assumption of normality and what can be done about it?

When the sample size is sufficiently large (>200), the normality assumption is not needed at all as the Central Limit Theorem ensures that the distribution of residuals will approximate normality. When dealing with very small samples, it is important to check for a possible violation of the normality assumption.