Data interpretation is the process of correctly extracting, analyzing meaningful information, or data from the various data sources such as charts, tables, graphs, etc, and arriving at a relevant logical conclusion.
What are the different Data Interpretation Methods?
Different DI methods are used by analysts to help people make sense of numerical data that has been collected, analyzed, and presented. There are two main ways in which data interpretation can be done:
- Quantitative methods
- Qualitative methods
The quantitative data interpretation method is used to analyze numerical data. This data type contains numbers and hence is analyzed with the use of numbers and not texts.
The statistical methods used in analyzing quantitative data are:
- Standard deviation
- Frequency distribution
- Regression analysis
- Cohort analysis
- Predictive and prescriptive analysis
The qualitative data interpretation method is used to analyze categorical data. This method uses texts, rather than numbers or patterns to describe data.