Let us see about **applied statistics** methods. Statistics referred to as the science. It is making successful use of numerical data relating to set of individuals or
experiments. It contracts with all parts of things. Not only the analysis and group. It also includes the planning of the group of data, in phrases of the design of reviews and
experiments.

It having four types of levels of measurement or measurement scales used in applied statistics methods:

- Nominal
- Ordinal
- Interval
- Ratio

They have various degrees of usefulness in applied statistics methods.

**Nominal measurements**

It contain no significant rank sequence amongst values.

**Ordinal measurements**

It contain inaccurate differences between successive values, but have a significant order to those values.

**Interval measurements**

It have significant distances between measurements described, but have no consequential zero value defined.

**Ratio measurements**

It have together a zero value described and the distances between different measurements classified. The maximum flexibility in applied statistical methods that will be used for examining the data.

Since variables conforming only to the nominal or ordinal measurements will not be logically measured.

**Null hypothesis**

Interpretation of applied statistical methods will frequently occupy the development of a null hypothesis in that the statement is that whatever is future as a reason has no effect on the variable measured.The most excellent illustration for a trainee is the predicament meet by a jury trial. The reflection comes because of suspicion of the guilt.

**Error**

Working from a null hypothesis the following two basic forms of error is standard:

**Type I errors**

These type of error occurs where the null hypothesis is incorrectly discarded giving a false positive.

**Type II errors**

In these type of error occurs where the null hypothesis fails to be discarded and a real difference between populations is missed.