Which type of data interpretation is less susceptible to differences of opinion?

Quantitative data interpretation is considered less susceptible to differences of opinion because it relies on numerical data and statistical analysis. This type of data provides objective measurements that can be verified and replicated, allowing for clear conclusions drawn from the data. For instance, when analyzing survey results or performance metrics, quantitative data enables analysts to see numerical trends and patterns that are straightforward and generally accepted.

Since quantitative data involves measurable variables, it minimizes the impact of personal biases or subjective viewpoints that may influence how data is perceived. This objective nature leads to a more consistent interpretation among different individuals, as the conclusions drawn are based on facts rather than personal feelings or interpretations.

In contrast, qualitative data interpretation may involve more variability, as it focuses on descriptions, themes, and non-numerical information that can be influenced by individual perspectives. Subjective data interpretation inherently allows for personal bias since it often reflects the individual's opinions or feelings. Comparative data interpretation may also involve varying opinions based on the aspects being compared, leading to different conclusions based on the context of the comparison.

Overall, the robustness and objectivity of quantitative data contribute to its reliability and lower susceptibility to differences of opinion, making it a preferred choice in scenarios where impartiality is critical.