P.11 Describing Data

Purpose of Data Description: A Comprehensive Study Guide

Goal:
To clearly and accurately communicate the trends, patterns, and details observed in your experimental results—often using a graph—to support your findings and conclusions.

1. Purpose of Data Description

Data description involves summarizing and interpreting the relationship between the independent variable (typically on the x-axis) and the dependent variable (typically on the y-axis) as visualized in a graph. This process provides evidence that supports your research findings and helps to:

• Highlight Trends: Show overall increases, decreases, or other patterns.
• Emphasize Relationships: Demonstrate how changes in one variable affect another.
• Provide Evidence: Use specific data points to back up your interpretation.

2. Steps for Describing Data from a Graph

a. Identify the Overall Trend

• What to Do:
  Begin by summarizing the general relationship between the variables.
• Example:
  “As rennin concentration increases, the time to reach the end-point decreases, indicating that the reaction rate increases with higher rennin concentration.”

b. Describe Gradient Changes

• What to Do:
  Note any variations in the slope (gradient) of the graph to explain how the rate of change varies.
• Example:
  “The gradient is steep at lower rennin concentrations, indicating a rapid increase in reaction rate, and becomes gradually less steep as the concentration rises, showing a diminishing effect on the reaction rate.”

c. Provide Specific Data Points

• What to Do:
  Quote key coordinates or notable data points from the graph.
• Example:
  “At 0.2% rennin, the mean time to end-point is 68.0 s, whereas at 1% rennin, the mean time decreases to 12.9 s.”

d. Assess Proportionality

• What to Do:
  Determine if the relationship is proportional (linear) or non-linear (curved).
• Example:
  “The graph forms a curve with a decreasing gradient, indicating that the relationship between rennin concentration and reaction rate is non-linear.”

3. Important Considerations When Describing Data

• Avoid Time-Based Language:
  Use precise language that accurately describes the data. If the x-axis is not time, avoid terms like “at first” or “quickly.” Instead, use phrases such as “at lower concentrations” or “with increasing concentration.”
• Quote Data with Precision:
  Provide exact x and y values for key points to support your description with concrete evidence.

4. Example of a Data Description

Using a hypothetical graph (e.g., Figure P1.6), a well-crafted data description might read:

“When no rennin was present, no reaction occurred, as indicated by an infinite time to reach the end-point. As rennin concentration increased, the mean time to reach the end-point decreased. For instance, at 0.2% rennin, the mean time was 68.0 seconds, whereas at 1% rennin, it was 12.9 seconds. The curve displays a decreasing gradient, which is steepest at lower concentrations and flattens at higher concentrations. This non-linear trend shows that increases in rennin concentration have a more pronounced effect on reaction rate at lower concentrations than at higher ones.”

5. Key Terms

6. Tips for Effective Data Description

• Use Accurate Vocabulary:
  Choose clear and precise language to describe trends, avoiding ambiguous terms.
• Highlight Key Trends:
  Focus on the major patterns and relationships in your data rather than minor fluctuations.
• Support with Data Points:
  Always back up your description with specific values or coordinates from the graph to provide evidence.

7. Conclusion

Describing data effectively involves more than stating numbers—it requires interpreting trends, assessing relationships, and clearly communicating these insights with supporting data. By following the structured steps and using precise language, you ensure that your data description is accurate, compelling, and directly tied to your research findings. This clarity strengthens your overall analysis and enhances the credibility of your conclusions.