• What not to do? Merely describe your results.
• What to do?
  • Discuss trends in your data. Refer to your graphs (e.g., "Graph 1", "Graph 2").
  • Make Comparisons: Check if your results align with accepted values or previous findings.
  • Explain with References: Compare your findings with established literature. Always cite your sources.
  • Empirical Evidence Reminder!: You can't prove a theory with your results. At best, you can say the data supports a theory.

🌍 Real-world Example: Imagine you’re testing whether athletes run faster on a full stomach. If you notice a trend where they run slower post-meal, you'd refer to studies or articles that might support this finding. Remember, just because it happened in your experiment doesn’t mean the meal was the only reason!

• It's okay to get unexpected results. Maybe your data doesn’t show a clear trend.
• Don't twist your findings to match your expectations.
• Instead, discuss your confidence level in the results.

🌍 Real-world Example: Think of an experiment to determine if listening to music improves concentration. If you don’t find a clear trend, don’t force it! Maybe other factors like the type of music or the volume played a role.

• Was the research valid? Were the measurements reliable?
• Evaluation should include:
  • Weaknesses in your study.
  • Significance of each weakness.
  • Suggested improvements.

🌍 Real-world Example: If you're testing how different shoes affect jump height and you only test one brand of shoes, a weakness might be a limited variety of shoes. The significance? Your results only apply to one brand. An improvement would be to include more brands!