Purpose of Explaining Results: A Comprehensive Study Guide

Goal

• Interpretation of Data:
  Explain how the observed trends or patterns in your data relate to established scientific theories or principles.
• Demonstration of Understanding:
  Show that you comprehend the underlying mechanisms driving the relationship between your independent and dependent variables.
• Evidence-Based Communication:
  Use clear evidence from your results (often via graphs) to support your explanation.

Importance

• Validation of Findings:
  Explaining your results validates that the experimental data is consistent with scientific principles.
• Linking Data to Theory:
  It bridges the gap between raw data and theoretical understanding, showing how the data supports or refutes your hypothesis.
• Highlighting Limitations:
  An explanation also allows you to address any limitations or external factors that may have influenced the results, providing a more balanced interpretation.

1. Example: Enzyme Activity in the Rennin Experiment

Observed Trend

• Observation:
  In the rennin experiment, an increase in rennin concentration correlates with a decrease in the time taken to reach the end-point.
• Interpretation:
  This trend suggests that higher concentrations of rennin accelerate the reaction rate.

Scientific Explanation

• Enzyme-Substrate Interaction:
  • Mechanism:
    Rennin acts as an enzyme that catalyzes the breakdown of casein in milk, leading to clot formation.
  • Increased Reaction Rate:
    More rennin means more active sites are available for binding with casein, resulting in more frequent enzyme-substrate collisions and a faster reaction.
• Saturation Point:
  • Graph Observation:
    At higher rennin concentrations, the curve flattens, indicating that the reaction rate increases at a diminishing rate.
  • Reason:
    This flattening occurs because the substrate (casein) becomes the limiting factor—most of it is already bound, so additional enzyme has a smaller effect.
• Optimal Conditions:
  • Controlled Variables:
    Maintaining constant temperature and pH ensures that the enzyme operates at maximum efficiency until substrate saturation is reached.

2. Key Points for Explaining Results

• Reference the Trend:
  Clearly state the relationship observed in the data.
  Example: “As rennin concentration increases, the time to reach the end-point decreases.”
• Scientific Reasoning:
  Connect the observed trend with scientific principles, such as enzyme kinetics, substrate availability, and reaction rates.
• Acknowledge Limitations:
  Mention any factors (like substrate limitations) that might affect the interpretation of your data.
• Use Precise Language:
  Avoid definitive terms like “proven” and instead use phrases such as “the data suggest” or “are consistent with” to indicate support for the hypothesis without overstating certainty.

3. Example Explanation Structure

Here is an example of a well-structured explanation for the rennin experiment:

“As rennin concentration increases, the time to reach the end-point decreases, indicating an increased reaction rate. This is because a higher concentration of rennin provides more active sites for casein binding, leading to more frequent enzyme-substrate collisions. However, at higher concentrations, the reaction rate shows diminishing returns due to substrate saturation—once most of the casein is bound, additional enzyme has little effect. Thus, while the data support that increased enzyme concentration accelerates the reaction, it also reveals that the effect levels off once substrate availability becomes limiting.”

4. Practical Tips for Explaining Results

• Consider Limitations:
  Briefly discuss any potential experimental limitations (e.g., environmental conditions, substrate availability) that might influence your results.
• Use Clear Scientific Terms:
  Incorporate terms like “enzyme-substrate collisions,” “saturation,” and “reaction kinetics” to connect your data with underlying biological or chemical processes.
• Link Theory to Observations:
  Always relate your observations back to established scientific theories and principles.
• Be Concise but Thorough:
  Your explanation should be clear and focused, covering all key aspects of the trend without introducing new data.

Conclusion

Explaining your experimental results is a vital component of the scientific process. By interpreting trends and linking them to underlying mechanisms, you not only validate your data but also demonstrate a deep understanding of the scientific principles at work. A well-crafted explanation ties your findings directly to your hypothesis and acknowledges any limitations, providing a comprehensive and balanced interpretation of your research.