Which type of statistics do not provide information about causality?

Descriptive statistics are primarily concerned with summarizing and organizing data to provide a clear understanding of a dataset. They offer valuable information such as measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation), and visual representations like charts and graphs. However, descriptive statistics do not address relationships between variables or explore whether one variable causes changes in another. Thus, they do not provide insights into causality or the underlying reasons for the observed patterns in the data.

In contrast, inferential statistics involve making predictions or inferences about a population based on sample data, and while they can indicate relationships, they often rely on assumptions that can suggest causal links under certain conditions. Correlational statistics specifically examine the relationship between two or more variables, determining how they may change together, but do not establish direct cause-and-effect relationships. Qualitative statistics involve non-numeric data that can provide deeper insights into people's experiences and contexts but similarly do not offer information about causality in a quantitative sense. Therefore, the nature of descriptive statistics makes them the best choice when considering which type does not provide information about causality.