Which type of data is less susceptible to differences of opinion when being interpreted?

Quantitative data is less susceptible to differences of opinion during interpretation because it consists of numerical values and objective measurements that can be analyzed statistically. This type of data provides concrete, measurable facts that are often the result of systematic processes, such as surveys, experiments, or observational studies. For instance, when analyzing sales figures or employee performance metrics, the evaluation is based on quantifiable data points that yield consistent results, regardless of individual bias or subjective perspectives.

In contrast, qualitative data is inherently more subjective, as it captures emotions, opinions, and experiences that can vary greatly among individuals. Subjective data relies heavily on personal interpretations, which can lead to significant differences in understanding and conclusions drawn from the same information. Feedback data, while potentially useful, may also encompass subjective opinions or insights, making it vulnerable to bias.

The robustness and objectivity of quantitative data make it a preferred choice in fields that require evidence-based decision-making, such as business planning, where accuracy and clarity are paramount.