Non-uniformity refers to the lack of consistency or homogeneity in a dataset. It can manifest as variations in formats or units used for measurements across different entries or inconsistency in naming conventions or categories assigned to variables.
Related terms
Inconsistencies: Inconsistencies refer to discrepancies or contradictions within a dataset that make it challenging to draw meaningful conclusions. They can occur due to human error, different data sources, or changes in data collection methods.
Standardization: Standardization is the process of transforming variables in a dataset to have a consistent scale or format. It helps to make data more comparable and facilitates analysis by removing non-uniformity caused by variations in units or formats.
Normalization: Normalization is a technique used to rescale numerical variables within a specific range. It ensures that variables with different scales do not introduce bias in analyses and addresses non-uniformity caused by variations in ranges of values.