Nominal Vs Ordinary Data
|
Characteristics |
Nominal data |
Ordinal Data |
|---|---|---|
|
Nature of Categories |
Distinct and Discrete |
Discrete and Distinct |
|
Order/Ranking |
No inherent order |
Has a clear order or ranking |
|
Numerical Values |
No meaningful numerical values |
No meaningful numerical values |
|
Analysis Techniques |
Frequency counts, percentages, bar charts |
Ranking, median, non-parametric tests, ordered bar charts, ordinal regression |
|
Example |
Colors, gender, types of animals |
School grades, education level, seniority level |
|
Interpretation |
Used for classification and grouping based on category |
Used for assessing ordered preferences, hierarchy, or rankings |
Nominal vs Ordinal Data
Data science revolves around the processing and analysis of data utilizing a range of tools and techniques. In today’s data-driven world, we come across types of data each requiring handling and interpretation. It is important to understand different types of data for proper data analysis and statistical interpretation. The type of data determines the proper statistical methods and operations that should be used. Various data types need different analysis and interpretation methods to draw significant conclusions. In this article we will explore the concept of data, and its significance provide real-world examples, and guide you through ways to work with it.