Ordinal data refers to an arrangement of data on a scale. A variable X involves the number of days that subjects have been fed a specific diet, and variable Y could measure these people's status in a race. In such data, it is possible to correspond to the effects of the variable Y.

Interval data, on the contrary, is a significant ongoing scale of management and the information is also at the interval level. This interval is how equal differences between values in scale relate to real differences between physical quantities that the scale intends to measure. Interval data can also be converted into ordinal data.

There are two types of data that are used in statistics. Interval and ordinal are those two types of data. They are a unit of measurement and help with quantifying data. Between the two, ordinal data is easier to understand and they have a sequence that flows naturally.

When it comes to the values, ordinal data focuses on the equality of how those values are positioned. Compared to interval data, ordinal data focuses on how two values are deviated from one another. When trying to find the difference between values, it is easy to do so. This type of data continues.

Ordinal and nominal are obviously two different types of data. When you say ordinal data, this means that you are concerned about the order and ranking of the different things that are available.

When you say interval data, this means that you are looking into the differences in the values that are available in one area.

When you are using ordinal data, you are going to think about the position that is already available on the scale while when you say interval data, this means that there are differences in the two values that are placed on the scale.

Interval and ordinal data are two data types used in statistics and a unit of measurement used in data quantities. Ordinal data are known to be characterized by a clear and natural sequence, ranking, or ordering on a scale. Ordinal data are more concerned with the position of values than equality or certainty between two values.

The scale of ordinal data is described to be non-uniform, and they have a defined category. Ordinal data are also described to be a kind of non-parametric data which are not easy to predict and do not take any specific pattern of distribution.

Conversely, interval data place more emphasis on the deviation between two values that follow themselves on a particular scale. It is quite easy to discover the difference between two values as there seems to be an equal interval between them. Interval data have a continuous and more meaningful scale of measurement.