Data Presentation Methods

• Raw data is typically hard to understand, so it needs to be summarized, processed and analyzed.
• Data needs to be presented effectively.
• Inappropriately presented data fails to convey information clearly.
• Planning data presentation is essential before processing raw data.
• Presentation methods should be determined by data format, analysis method, and information to emphasize.
• Data presention can be: text, tabular or graphical.

Text

• Is the main method of conveying information to explain results, trends, and context.
• Data is mainly presented sentences or paragraphs.
• Text can provide interpretation or emphasize data.
• Written language is better when conveying one or two quantitative data numbers,
• Tables/graphs take up too much space without enhancing understanding if presenting one or two quantitative data numbers.
• Tables or graphs are more appropriate if presenting more data or data trends.
• Data reads slower in text and a long list of information can make it harder to understand.
• Woody cover information can be presented using numbers.
• For example woody cover comprised 50% of landcover in 2000 and 10% in 2010.
• Meaning there was considerable woody cover decrease between 2000 - 2010.

Tables

• Tables turn information into words or numbers in rows and columns.
• Anyone with a sufficient level of literacy can easily understand a table.
• Tables are best for presenting individual information and can show quantitative and qualitative data.
• Tables strength is the ability to accurately present information that cannot be shown with a graph.
• Information using different units can be presented together
• Tree height, biomass, and canopy size can be in the same table
• Tables effectively summarize and compare quantitative information across different variables.
• Tables are not suitable for studying data trends since interpreting information takes longer than graphs.
• It is hard to identify the information since all data is of equal importance.

Heat Maps

• Heat maps enable better visualization of information than tables.
• Colors can be applied to the heat maps background of cells to better visualize data presented in tables.
• Color adjustment help readers identify the information more quickly.
• Excel has features for making heat maps using the conditional formatting menu.

Graphs

• Graphs simplify complex information using images and emphasize data patterns or trends.
• Useful for summarizing, explaining or exploring quantitative data
• Graphs are effective for presenting large data amounts, but can replace tables for small data sets.
• Selecting an ideal graph format is important for reader comprehension.

Scatter Plots

• Data is presented on the x and y axes.
• They are used to investigate the association between two variables
• Each point represents an individual item.
• Associations can be studied by analysing patters across all points.
• A regression line can be added to determine if the association can be explained.

Bar Graphs

• Bar graphs are used to indicate and compare values in a discrete category or group.
• Includes frequency or measurement parameters.
• Bars can be created vertically or horizontally.
• The height of a bar indicates an amount of information in a category.
• Bar graphs are flexible and can also be in a grouped form if multiple data sets are present.
• The mean and standard deviation are expressed as whiskers on the bars.
• Endpoint comparisons help identify smallest and largest categories to show gradual differences.

Histograms

• Histograms are a bar chart used to show statistical information.
• They represent the frequency distribution of continuous data.
• They show the number of observations within a range of values, known as classes or bins.
• Histograms help display numerical data by rendering vertical bars.
• It allows comparison of non-discrete values
• Number of warthogs can be visualized using a histogram chart in various weight ranges

Stacked Vertical Bar Graphs

• A vertical bar graph is a stacked vertical bar graph.
• Used for comparing the sum of each category and analyzing parts of a category
• Stacked vertical bar graphs are excellent for visualization, although comparisons of various category parts are challenging due to the lack of a reference line.

Line Plots

• Line plots represent time-series data like monthly precipitation and yearly unemployment rates.
• In other works, variables observed over time.
• Line graphs are useful for studying patterns and data trends that include climate influences, large changes or turning points.
• Are also appropriate for measured data progression of a continuous variable.
• If data is collected at regular intervals, values in between the measurements can be estimated.
• The x-axis represents the continuous variable, while the y-axis represents the scale and the measurement values.
• Additionally useful for analyzing patterns, multiple sets can represented on a single line graph.

Box and Whisker Charts

• They are appropriate for presenting nonparametric data, because they show variations in samples of a population without making assumptions about the underlying statistical distribution.
• Include boxes that represent interquartile range and the median with whiskers outside of the boxes.
• Whiskers present the largest and smallest data values or a part of the data.
• Excluded data points are called outliers, and shown as individual points.
• The spacing at both ends indicates dispersion in data.
• The relative location of the median shows skewness.