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6.5 Criterion D - Evaluation

Tags
Criterion D assesses the critical analysis of your investigation, focusing on its strengths, limitations, and suggestions for improvement. It evaluates how well you identify weaknesses in your methodology and propose realistic solutions that address those issues.
Marks
Descriptor
0
The report does not reach a standard described by the descriptors below.
1-2
The report states generic methodological weaknesses or limitations. Realistic improvements to the investigation are stated.
3-4
The report describes specific methodological weaknesses or limitations. Realistic improvements to the investigation that are relevant to the identified weaknesses or limitations, are described.
5-6
The report explains the relative impact of specific methodological weaknesses or limitations. Realistic improvements to the investigation, that are relevant to the identified weaknesses or limitations, are explained.

1. Identifying Strengths & Weaknesses

Critically evaluate your experiment by examining its strengths and weaknesses. Be honest but balanced—highlight what worked well while identifying areas for improvement. Don’t just list issues; analyze how they impacted your results.
Example Strength: “The use of a digital thermometer ensured accurate temperature readings.”
Example Weakness: “Inconsistent light intensity due to fluctuating ambient conditions introduced variability.”
Systematic Errors
Random Errors
Identify consistent biases or issues in the experimental design or equipment that affected accuracy
Highlight unpredictable factors that caused variability in results
Example: "The pH meter was not calibrated, leading to consistently inaccurate readings.”
Example: "Air currents in the room affected the rate of water evaporation during different trials.”
Key Questions to Guide Your Critique
1.
Were the variables controlled effectively?
“While temperature was controlled using a water bath, variations in substrate concentration may have influenced the enzyme activity.”
2.
Was the method precise and reliable?
“The gas syringe measured oxygen accurately, but minor leaks may have affected the total volume collected.”
3.
Were there any procedural flaws?
“The limited range of pH values (5–7) restricted the ability to identify the full effect of pH on enzyme activity.”

2. Proposing Realistic Improvements

Identify design flaws or factors that compromised the data quality and propose practical solutions. Ensure suggestions are specific and address the exact issue, avoiding vague or unrealistic recommendations.
Improvement
Example
Address inconsistencies or equipment limitations.
“Calibrate all measurement tools, such as the pH meter, before starting trials to reduce systematic errors.”
Provide practical steps to improve precision, reliability, and validity.
“Use a thermostatically controlled water bath to maintain a consistent temperature.”
Suggest extending the scope to better address research questions.
“Incorporate a wider range of pH values (e.g., 3–10) to observe the full effect on enzyme activity.”

3. Acknowledging Scope of Limitations

In this section, evaluate the limitations of your investigation, focusing on how they affected your ability to fully address the research question (RQ). Identify weaknesses, their causes, and their impact on the results and conclusions.
What to Include
Command
Example
Evaluate the Extent to Which the RQ Was Answered
Reflect on whether your experiment provided sufficient and reliable data to address the RQ comprehensively.
“The data collected demonstrated a clear trend in the effect of pH on enzyme activity, but the limited range of pH values restricted a complete understanding of the optimal range for the enzyme.”
Identify Aspects of the Experiment That Failed to Address the RQ
Highlight any gaps where the method or data did not align with the intended objectives.
“While the effect of light intensity on photosynthesis was investigated, inconsistencies in maintaining CO₂ levels may have introduced additional variability, limiting the precision of the results.”
Discuss the Impact of Limitations on Conclusions
Explain how the identified limitations influenced the reliability or generalizability of your conclusions.
“The small sample size limited the statistical validity of the findings, making it difficult to draw definitive conclusions about the relationship between substrate concentration and enzyme activity.”
Assess the Experimental Scope and Breadth
Consider whether the experimental design captured the full complexity of the biological system being studied.
“The experiment only included three trials per condition, reducing the robustness of the data. Increasing the number of replicates would provide greater confidence in the observed trends.”
Sample Evaluation: