• Before getting into this, you need to know How to calculate simple statistics.

How a Non-Parametric business scenario differs from a Parametric Business scenario

A non-parametric business scenario differs from a parametric business scenario in that:

1. a non-parametric business scenario provides small samples, poor data, and the variables of interest, (for example, the distribution of the mean and/or standard deviation of the variables within the scenario population, is not known or identified).

Whereas in a parametric business scenario:

1. the distribution of the mean and/or standard deviation of the variables are known, and we have supporting data to estimate the mean and standard deviation to describe the variables in a population.
2. Furthermore, in a non-parametric scenario, we cannot depend on the estimation of the mean and standard deviation to describe the variables in the population of the business scenario. Therefore, we use specific methods that only pertain to particular non-parametric scenarios, which are fundamentally similar, but are different methods from those used on parametric scenarios.

For example, if we used the Sign-test method for a non-parametric business scenario to compare dependent variables of a sample, we would use a comparable method of the T-test method in a parametric business scenario to compare dependent variables of a sample.

In non parametric statistics, the (parameters) central tendencies and the distributions of a population are:

• unknown, or
• unidentifiable

It would be very difficult or impossible to identify the values of the variables of the whole business scenario population without such central tendencies/parameters or knowing the distribution or shape.

Also, the estimate values of variable data is not as accurate or efficient with non parametric methods as parametric methods, because they can lack

• accuracy in telling us the truth, where there is in fact the truth and more likely to cause a TYPE I error.

Therefore they don't depend on

1. any estimate of mathematical distribution of the mean, or
2. standard deviation in a population (but we can depend on the estimates for a sample of the population).

Number One distinction:

• We cannot make assumptions of normality when we have non-parametric data.
• Therefore, it would be safe to say that we cannot depend on the assumptions (in a non parametric business scenario) of the mathematical distribution of the central tendencies in a population.