Descriptive statistics typically help researchers to understand what aspect of their data?

Descriptive statistics play a crucial role in summarizing and presenting the essential features of a dataset. They allow researchers to quantify and communicate key characteristics of their data, such as the central tendency and variability. Specifically, metrics like the mean, median, and mode provide insights into the average values and the most common observations within a dataset. This summary helps researchers get a clear understanding of the overall trends and patterns present in their data.

In contrast, the other choices focus on different aspects of statistical analysis. The statistical power of studies pertains to the likelihood of detecting an effect if there is one, which involves inferential rather than descriptive statistics. External validity relates to how generalizable the study's findings are to broader contexts, again leaning more toward inferential statistics. Lastly, the predictability of variable relationships deals with establishing and understanding correlations or causal links between variables, highly reliant on inferential methods rather than descriptive summarization. Thus, the choice that highlights the mean, median, and mode directly captures the essence of what descriptive statistics provide.