A Skewed or Noncentral T Distribution is a statistical distribution that is used to model data that is not normally distributed. Unlike the standard T Distribution, which assumes that the data is symmetrically distributed around the mean, the Skewed or Noncentral T Distribution allows for a non-zero mean and greater variability in the data.
The main difference between a Skewed or Noncentral T Distribution and a Normal Distribution is that the former allows for a non-zero mean and greater variability in the data. This means that the Skewed or Noncentral T Distribution can better model data that is not normally distributed, while the Normal Distribution assumes that the data is symmetrically distributed around the mean.
A Skewed or Noncentral T Distribution should be used when the data being modeled is not normally distributed. This could be due to a non-zero mean or greater variability in the data. It is also commonly used when there are outliers in the data that would significantly affect the results if a Normal Distribution was used.
One of the main advantages of using a Skewed or Noncentral T Distribution is that it can better model data that is not normally distributed. This can lead to more accurate results and better insights into the data. Additionally, the Skewed or Noncentral T Distribution can handle outliers better than the Normal Distribution, making it a more robust choice for certain types of data.
A Skewed or Noncentral T Distribution is calculated using the same formula as the standard T Distribution, but with additional parameters for the non-zero mean and greater variability in the data. This calculation can be done manually using statistical software or through the use of online calculators or tables.