To bring something into conformity with a standard, norm, or convention; to make something normal or regular. This often involves adjusting data, processes, or behaviors to a common scale or reference point, enabling comparisons, simplifying complex situations, and ensuring consistency. In statistical contexts, it might refer to adjusting data to a standard distribution. In social contexts, it can imply the acceptance of something that was once considered unusual or unacceptable. It can also refer to the process of regularising something, for instance, a business, by complying with accepted standards and practises and bringing that business out of a state of being unregulated.
Normalised meaning with examples
- To compare sales figures across different regions, the data was normalised by population size, giving us a more accurate understanding of performance. This allowed us to see the regional trends better. Previously the sales in one region had always masked sales in another. Sales can also be looked at as profit and expenses, for better performance and understanding.
- The company's project management procedures were normalised across all departments, streamlining communication and project delivery. It created uniformity in the company's workings and the ability to better use resources and track progress. This allowed for better comparison of costs and revenue, helping in budgeting for future projects. The ability to move people to different projects without major learning periods was also realised.
- After years of social debate, discussions about mental health have been normalised in the media and everyday conversation, reducing stigma. This helps people accept their own issues with a sense of comfort. This has also allowed for easier access to help and better education within schools and workplaces. This allows for people to feel more at ease about mental health discussions, helping to break down stigma.
- The software package normalised the dataset, creating a common field to allow comparison and analysis of all the data. This allowed for an output that provided more information. This allowed data from various origins and formats to be merged, and then to be presented in a cohesive way to allow further processing. This resulted in clearer understanding of results.