Describe the impact of skewness and kurtosis on parametric testing.
- They can improve the accuracy of parametric testing.
- They can invalidate the results of parametric testing.
- They can reduce the variance in parametric testing.
- They do not impact parametric testing.
Skewness and kurtosis can invalidate the results of parametric testing. Many parametric tests assume that the data follows a normal distribution. If the data is highly skewed or has high kurtosis, these assumptions are violated, and the test results may not be valid.
Loading...
Related Quiz
- In the context of handling missing data, what does 'imputation' mean?
- How would the mean change if an additional number far away from the current mean were added to the dataset?
- Given a set of data that follows a Binomial Distribution, how would you estimate the parameters of the distribution?
- What aspects should be considered to improve the readability of a graph?
- You've created a histogram of your data and you notice a few bars standing alone far from the main distribution. What might this suggest?