When assumptions are violated…
Sometimes we are confronted with data that are not normally distributed, and thus violate a major assumption of certain tests (e.g. t-test). Likewise, other test assumptions, such as equal variances, are not always upheld in nature. This page reviews some of the options for dealing with violations of assumptions.
What happens when assumptions aren't met?
When we collect samples and make inferences from them there are certain assumptions that must be met. Some of these are to ensure that our estimates are unbiased. Other assumptions are based on the assumed distribution of the test statistic. If we want to use a distribution to calculate a P-value our data must meet the assumptions of the distribution.
Additional Resources
Testing for normality
Intro/Intermediate: Using graphs and normality tests to deduce whether data have been sampled from a normal distribution
Testing for normality in R
Intermediate: Worked example using Shapiro-Wilk test in R
Review Questions
What are two types of plots you can use to check if your data are from a population that is normally distributed?
What is a non-parametric alternative to the two-sample t-test?
What are the assumptions of non-parametric tests?
The Next Steps
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