What is the Difference Between Parametric and Non Parametric?

The main difference between parametric and non-parametric methods lies in the assumptions they make about the underlying population distribution.

Parametric methods are based on assumptions about the distribution of the population from which the sample was taken. They typically require that the data follows a specific distribution, such as a normal distribution. Some key characteristics of parametric methods include:

• Rely on assumptions about the shape or parameters of the population distribution.
• Most common parametric assumption is that data are approximately normally distributed.
• Require less data compared to non-parametric methods.
• Perform well when assumptions are met, but can lead to incorrect conclusions if assumptions are strongly violated.

Non-parametric methods, on the other hand, do not rely on assumptions about the shape or parameters of the underlying population distribution. They are more flexible and can handle a wider range of situations. Some key characteristics of non-parametric methods include:

• Do not assume anything about the underlying distribution.
• Applicable for both variables and attributes.
• Generally require more data than parametric methods.
• Can be used with data that has outliers.
• Perform well in many cases and are necessary in some, but they are not a perfect solution.

In summary, parametric methods are more powerful and efficient when the data meets the assumptions, while non-parametric methods are more flexible and can handle a wider range of situations. If the assumptions of a parametric method are not valid, it is recommended to use an analogous non-parametric method instead.

Comparative Table: Parametric vs Non Parametric

Here is a table comparing parametric and non-parametric methods:

Parametric methods make assumptions about a population's parameters and assume a normal distribution or can approximate it using a normal distribution. They require less data than non-parametric methods and perform well when assumptions are met. On the other hand, non-parametric methods do not assume anything about the underlying distribution, require more data than parametric methods, and can be used for various data types, including ordinal, nominal, and interval (continuous) data. They are also less affected by outliers and can be more flexible in modeling with confounding factors.