# Data Presentation

**Defining Data Presentation**

- Data presentation is the process of
**communicating data and information**in a clear, easy to understand manner. - Includes a vast array of techniques, ranging from tables and charts, to graphs and diagrams.
- The method of presentation often depends on the type of data and the information that needs to be communicated.

**Types of Data Presentation**

**Tabular Format**: Presents data in rows and columns making it easy to compare different variables or categories.**Bar Chart**: Suitably used to compare quantities of different categories.**Histogram**: Represents distribution of a single quantitative variable, useful for showing patterns such as central tendency and dispersion.**Pie Chart**: Excellent for illustrating relative proportions of different categories within a total sum.**Line Graph**: Ideal for showing trends over an interval or time period.**Box Plot**: A useful tool to depict variation and measures of central tendency with a single quantitative variable.**Scatter Plot**: Depicts relationship between two quantitative variables, useful for correlation analysis.

**Key Traits of Effective Data Presentations**

**Clarity**: Data should be presented in a way that the message is clear, minimising potential confusion or misinterpretation.**Accuracy**: The data presentation should truthfully represent the data. Misrepresentations risk misleading conclusions.**Simplicity**: The presentation should be simple and straightforward. Over-complication might cloud the key points.**Relevance**: The chosen form of presentation should be relevant to the data and the messages being communicated.

**Interpreting Data Presentations**

- Ability to interpret the graphical representation is equally important to creating the data presentation.
- Understand common terms of statistics including
**mean**,**median**,**mode**,**range**and**standard deviation**. - Able to make and justify conclusions based on the presented data, e.g., noting trends, outliers, clusters from a scatter plot.
- Comprehend the assumptions, like a linear relationship in scatter plots or normal distribution in histograms.
- Always back interpretations with actual data and calculations, not merely an overall eyeball view.
- Involve skills such as mapping bar chart heights to frequency, or pie-chart sectors to proportions.
- Understand how to make comparisons such as distribution differences between groups using data presentations.