Recording and displaying results

Recording and displaying results is a fundamental practical skill in scientific investigations, crucial for organizing, interpreting, and communicating experimental findings effectively. It is part of the broader practical skills assessed in examinations.

Recording Data

Results, particularly quantitative (numerical) data, must be recorded systematically.

• Results Tables:

  • Structure: Tables should be clearly drawn with ruled columns, rows, and a border to ensure clarity and easy readability. They should include enough rows and columns for all necessary data, including a column for processed data like means.

  • Headings and Units: Each column must have a clear heading, including the quantity and its unit. Units should be in the column heading only, not within the table itself.

  • Variable Placement: The independent variable should be in the left-hand (first) column, followed by the dependent variable readings.

  • Consistency: Measurements of the dependent variable should be recorded to a consistent number of decimal places or significant figures, appropriate to the measuring instrument used.

  • Order: Results should be organized in a sensible sequence, often from lowest to highest values of the independent variable.

  • Repeats: It is highly recommended to take several repeat measurements (e.g., at least three) for each value of the independent variable. This helps reduce the effect of random error, making results more precise and demonstrating repeatability. A mean should be calculated from these repeats, excluding anomalous results.

  • Anomalous Results: Results that do not fit the overall trend should be investigated and, if a clear cause is identified, can be ignored when calculating means. Repeating experiments makes anomalous results easier to spot.

• Data Loggers: These electronic devices with sensors can record data over time, sometimes connected to a computer, which can aid data collection.

• Qualitative Observations: When recording non-numerical data (e.g., color changes), use simple, clear language (e.g., "red," "purple," "green," "dark green") and state the actual color observed. Using a scale (e.g., +, ++ for palest/darkest) with a key can also be useful.

Displaying Data

Presenting data visually makes it easier to understand results and spot trends.

• General Graph Construction Rules:

  • Axes: The independent variable is plotted on the x-axis (horizontal), and the dependent variable on the y-axis (vertical).

  • Labels and Units: Each axis must be fully labeled with the quantity and its units.

  • Scale: Choose sensible and equal intervals for the scales on each axis (e.g., 1s, 2s, 5s, 10s). The scales should cover the entire range of plotted values and make best use of the graph paper (at least half the graph paper). Starting the scale at zero is not always necessary.

  • Plotting Points: Points should be plotted neatly as small crosses (✕) or encircled dots (⊙). Avoid simple dots that might be obscured by lines.

  • Reading Values: Use a ruler to accurately read values off graphs, both horizontally and vertically from the axes.

• Types of Graphs and Charts:

  • Line Graphs: Used for continuous data on both axes, showing a smooth, numerical relationship. A best-fit line (straight or curved) should be drawn to show the trend, passing through or as near to as many points as possible, ignoring anomalous results. Points are generally not joined together unless the trend is uncertain or specific changes between data points are important. Extrapolation (extending the line beyond plotted points) is generally incorrect, unless you are certain the dependent variable would be zero at the origin.

  • Bar Charts: Used for qualitative (non-numerical) or discrete data where categories are distinct. Bars do not touch.

  • Histograms (Frequency Diagrams): Used for continuous independent variables where the area of the bars represents frequency. Bars touch.

  • Scattergrams (Scatter Graphs/Diagrams): Used to show the relationship (correlation) between two numerical variables. A line or curve of best fit can be drawn to show the trend.

  • Kite Diagrams: A specialized way to display distribution and abundance of species, often used in ecological surveys (e.g., on a rocky shore).

• Error Bars: Graphs can include error bars (representing standard deviation or standard error) to indicate the spread of results around the mean, aiding in assessing precision and significant differences between data sets.

These practices ensure that collected data is well-organized, visually comprehensible, and ready for further analysis and interpretation.