Displaying data

Displaying data is a crucial step in scientific investigation, serving to make your results easier to understand and to help you spot any trends or patterns. After quantitative results have been systematically recorded in tables, choosing the appropriate method to present them, such as a graph or chart, is essential.

General Principles for Displaying Data

• Clarity and Understanding: The primary purpose of displaying data is to allow for easy interpretation and identification of trends.

• Design Before Experimentation: While recording results in tables should be done beforehand, the choice of display method often depends on the type of data collected and the relationships you want to highlight.

• Accuracy and Precision: Proper scales, labeling, and plotting contribute to the accuracy and precision of the visual representation.

Types of Graphs and Charts

The choice of graph or chart depends on the type of data being presented:

• Line Graphs:

  • Used when both variables are continuous. This means there's a smooth, numerical relationship between the values on both axes.

  • The independent variable (the one you changed) is plotted on the x-axis (horizontal).

  • The dependent variable (the one you measured) is plotted on the y-axis (vertical).

  • Each axis must be fully labeled with the quantity and its unit. Units should only appear in the column heading or axis label, not within the data entries themselves.

  • Scales on each axis must go up in equal and sensible intervals (e.g., 1s, 2s, 5s) and should cover at least half the graph paper.

  • Points are typically plotted as small crosses or encircled dots.

  • A best-fit line (or curve) should be drawn smoothly to show the trend, passing through or as near to as many points as possible, while ignoring anomalous results. The line does not necessarily need to pass through the first or last point.

• Bar Charts:

  • Used for qualitative (non-numerical) data (e.g., blood group) or discrete data (numerical data that can only take certain specific values, like the number of patients).

  • The categories are plotted on the x-axis.

  • Bars are typically spaced out evenly with the same width.

• Histograms (Frequency Diagrams):

  • Used for displaying frequency data when the independent variable is continuous.

  • Unlike bar charts, the bars in a histogram touch each other.

  • The area of the bars represents the frequency (rather than just the height), and the height is called the frequency density.

• Scattergrams (Scatter Graphs/Diagrams):

  • Used to show the relationship or correlation between two numerical variables.

  • A line of best fit can be drawn to illustrate the trend, also ignoring anomalous results.

• Pie Charts:

  • Can also be used to present qualitative data or discrete data.

• Kite Diagrams:

  • A specific type of diagram used to show data from a belt transect, representing the distribution and abundance of species.

By adhering to these guidelines, displayed data provides a clear visual representation of experimental findings, making patterns and relationships evident for further analysis and conclusion drawing.