What type of data is primarily numerical and used for statistical analysis?

Quantitative data is fundamentally numerical in nature and is primarily used for statistical analysis. This type of data enables researchers to quantify variables, analyze relationships, and derive insights through mathematical techniques. Quantitative data can include various measures such as counts, percentages, means, and other statistics that allow for comparisons and inferences.

Using statistical methods on quantitative data can lead to clearer, objective conclusions about patterns or trends within the data. For example, researchers can conduct hypothesis testing or regression analysis to determine how one variable may influence another, demonstrating the power and importance of quantitative data in both marketing research and broader contexts.

In contrast, qualitative data focuses on descriptive attributes and characteristics rather than numbers, making it less suitable for statistical analysis. Primary data refers to information collected firsthand for specific research purposes, while secondary data represents previously gathered information used for new investigations. Both primary and secondary data can be either qualitative or quantitative, but it is quantitative data that is specifically required for rigorous statistical analysis.