Explain the difference between nominal and ordinal data with examples.

Nominal and ordinal data are two types of categorical data that are used in statistics and research.

1. Nominal Data: This type of data is used to label variables without providing any quantitative value. It's the simplest form of data that isn’t ordered or measured. Nominal data is often called named data - a common example is gender (male, female, other). In this case, there is no order between the categories. Another example could be the color of a car (red, blue, green, etc.). Again, there is no specific order to these colors, they are simply names of colors.

2. Ordinal Data: This type of data, on the other hand, does have a clear ordering. The categories can be ranked from smallest to largest, but the differences between the categories are not known. For example, a survey might ask respondents to rate a restaurant on a scale from 1 to 5. Here, 1 might represent 'very dissatisfied', 2 'dissatisfied', 3 'neutral', 4 'satisfied', and 5 'very satisfied'. The numbers have an order or rank; that is, 5 is more than 4, and 2 is less than 3. However, the exact differences between the ratings are not known.

In summary, the main difference between nominal and ordinal data lies in the fact that ordinal data has a certain order or rank, while nominal data does not.