Which of the following techniques can help structure your data story?

The option that accurately identifies techniques to help structure a data story includes correlations or connections, trends, comparisons, outliers, and unexpected results. These elements are fundamental in crafting a narrative around data as they provide insights into relationships and patterns.

Correlations or connections allow analysts to draw meaningful associations between different data points, helping to narrate how one aspect affects another. Trends reveal the overall direction in which data points are moving over time, which is crucial for understanding the context of the data. Comparisons enable the evaluation of different groups or variables against each other, highlighting differences and similarities that could be vital for decision-making.

Outliers, or data points that deviate significantly from the rest, can indicate critical insights or anomalies that deserve investigation, making them essential for a comprehensive understanding of the data landscape. Unexpected results can lead to valuable discoveries, challenging assumptions and prompting further inquiry into the data.

In contrast, the other choices consist of elements that either present a lack of analysis (like random values or static trends) or focus on rigid structures (equal distribution or fixed intervals) that do not facilitate a dynamic understanding of the data story. The effective communication of insights hinges on the ability to engage with and analyze the data creatively, which is embodied in the techniques listed