When assumptions are violated what do we use?

As we have already discussed, to use a one-sample t-test, you need to make sure that the data in the sample is normal or at least reasonably symmetric. In particular, you need to make sure that the presence of outliers does not distort the results.

How do you know if assumption is violated?

Potential assumption violations include:

1. Implicit factors: lack of independence within a sample.
2. Lack of independence: lack of independence between samples.
3. Outliers: apparent nonnormality by a few data points.
4. Nonnormality: nonnormality of entire samples.
5. Unequal population variances.

What happens when t test assumptions are violated?

If the assumption of normality is violated, or outliers are present, then the t test may not be the most powerful test available, and this could mean the difference between detecting a true difference or not. A nonparametric test or employing a transformation may result in a more powerful test.

What happens if 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.

Is normality required for regression?

Regression only assumes normality for the outcome variable. Non-normality in the predictors MAY create a nonlinear relationship between them and the y, but that is a separate issue. You have a lot of skew which will likely produce heterogeneity of variance which is the bigger problem.

What happens if independence assumption is violated?

In simple terms, if you violate the assumption of independence, you run the risk that all of your results will be wrong.

What do you do if Shapiro Wilk is significant?

If the Sig. value of the Shapiro-Wilk Test is greater than 0.05, the data is normal. If it is below 0.05, the data significantly deviate from a normal distribution.

What if data is not homogeneous?

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 are Assumption tests?

Assumption testing of your chosen analysis allows you to determine if you can correctly draw conclusions from the results of your analysis. You can think of assumptions as the requirements you must fulfill before you can conduct your analysis.

Why normality assumption is important in regression?

Making this assumption enables us to derive the probability distribution of OLS estimators since any linear function of a normally distributed variable is itself normally distributed. Thus, OLS estimators are also normally distributed. It further allows us to use t and F tests for hypothesis testing.