Contents

- 1 What happens if homogeneity of variance is violated?
- 2 What happens if one of the assumptions for Anova is violated?
- 3 What do you do when data fails test for homogeneity of variance?
- 4 What happens when normality assumption is violated?
- 5 How do you know if variances are equal or unequal?
- 6 What do you do when regression assumptions are violated?
- 7 What are the four assumptions of Anova?
- 8 What are the three assumptions for validity of the F test in the one way Anova?
- 9 What are the three assumptions of one way Anova?
- 10 What is the difference between the one way Anova F test and the Levene test?
- 11 What should I do if data is not normal?
- 12 What to do if Levene’s test of equality of error variances is significant?
- 13 Would it be safe to use the T procedures even if the normality assumption was seriously violated?
- 14 What are assumption violations?
- 15 What happens if linear regression assumptions are violated?

## What happens if 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.

## What happens if one of the assumptions for Anova is violated?

If the populations from which data to be analyzed by a one -way analysis of variance ( ANOVA ) were sampled violate one or more of the one -way ANOVA test assumptions, the results of the analysis may be incorrect or misleading.

## What do you do when data fails test for homogeneity of variance?

So if your groups have very different standard deviations and so are not appropriate for one-way ANOVA, they also should not be analyzed by the Kruskal-Wallis or Mann-Whitney test. Often the best approach is to transform the data. Often transforming to logarithms or reciprocals does the trick, restoring equal variance.

## What happens when normality assumption is violated?

For example, if the assumption of mutual independence of the sampled values is violated, then the normality test results will not be reliable. If outliers are present, then the normality test may reject the null hypothesis even when the remainder of the data do in fact come from a normal distribution.

## How do you know if variances are equal or unequal?

An F-test (Snedecor and Cochran, 1983) is used to test if the variances of two populations are equal. This test can be a two-tailed test or a one-tailed test. The two-tailed version tests against the alternative that the variances are not equal.

## What do you do when regression assumptions are violated?

If the regression diagnostics have resulted in the removal of outliers and influential observations, but the residual and partial residual plots still show that model assumptions are violated, it is necessary to make further adjustments either to the model (including or excluding predictors), or transforming the

## What are the four assumptions of Anova?

The factorial ANOVA has a several assumptions that need to be fulfilled – (1) interval data of the dependent variable, (2) normality, (3) homoscedasticity, and (4) no multicollinearity.

## What are the three assumptions for validity of the F test in the one way Anova?

The Three Assumptions of ANOVA ANOVA assumes that the observations are random and that the samples taken from the populations are independent of each other. One event should not depend on another; that is, the value of one observation should not be related to any other observation.

## What are the three assumptions of one way Anova?

What are the assumptions of a One – Way ANOVA?

- Normality – That each sample is taken from a normally distributed population.
- Sample independence – that each sample has been drawn independently of the other samples.
- Variance Equality – That the variance of data in the different groups should be the same.

## What is the difference between the one way Anova F test and the Levene test?

One method is the Bartlett’s test for homogeneity of variance (this test is very sensitive to non-normality). The Levene’s F Test for Equality of Variances, which is the most commonly used statistic (and is provided in SPSS), is used to test the assumption of homogeneity of variance.

## What should I do if data is not normal?

Many practitioners suggest that if your data are not normal, you should do a nonparametric version of the test, which does not assume normality. From my experience, I would say that if you have non – normal data, you may look at the nonparametric version of the test you are interested in running.

## What to do if Levene’s test of equality of error variances is significant?

If you have non-normal data and unequal population variances, transform the raw data to normal quantiles first, then test again for equal variances. If the variance test is still significant, use Welch’s Test on the transformed data.

## Would it be safe to use the T procedures even if the normality assumption was seriously violated?

When t test assumptions are violated. 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.

## What are assumption violations?

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

## What happens if linear regression assumptions are violated?

Linearity Linear regression is based on the assumption that your model is linear (shocking, I know). Violation of this assumption is very serious–it means that your linear model probably does a bad job at predicting your actual (non- linear ) data.