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Data visualization is important in statistics for several reasons. Most importantly, it allows us to deliver compound information and patterns in a way that is easy to understand and interpret. When we work with large sets of data, charts and graphs can help us identify trends, outliers, and other patterns that might not be immediately clear from just looking at the raw numbers (Brush et al., 2022).

The goal of these visualizations is to uncover insights, patterns and trends in data that may not be immediately obvious from raw numbers alone. Charts allow large datasets to be summarized and made more interpretable. They enable faster comprehension and improved decision making based on the data. Statistics would be much more difficult to apply effectively without the power of data visualization.

Some common types of charts and graphs used in statistics include:

1. Bar charts: These are great for comparing categorical data or showing how the frequency of a particular outcome may vary over a period.

2. Line charts: These are useful for displaying trends over time or showing how two variables are related to each other.

3. Scatterplots: These are good for showing the relationship between two variables and identifying outliers.

4. Pie charts: These are useful for showing the proportion of a whole that is divided into different categories.

5. Histograms: These are great for displaying the frequency distribution of a continuous variable.

The goal of these visualizations is to uncover insights, patterns and trends in data that may not be immediately obvious from raw numbers alone. Charts allow large datasets to be summarized and made more interpretable. They enable faster comprehension and improved decision making based on the data. Statistics would be much more difficult to apply effectively without the power of data visualization.