Designing investigations, concluding and evaluating

Welcome to the Lab! Designing and Perfecting Physics Investigations

Physics isn't just about memorizing equations from a data booklet; it’s about figuring out how the world works through experimentation. Whether you are working on your Internal Assessment (IA) or preparing for Paper 1B, knowing how to design a solid experiment and evaluate your findings is a superpower. In these notes, we will break down the process of Inquiry into manageable steps.

Don’t worry if this seems like a lot of "technical" steps at first. Think of it like a recipe: once you know the basic ingredients, you can cook up any investigation you want!

Part 1: Inquiry 1 – Exploring and Designing

Every great discovery starts with a question. In Physics, we call this the Research Question (RQ). To make it scientific, it must be specific and measurable.

1. Identifying Your Variables

To keep an experiment "fair," we use three types of variables:

• Independent Variable (IV): The one factor you deliberately change (e.g., the length of a pendulum).
• Dependent Variable (DV): The factor you measure to see how it responds (e.g., the time period of the swing).
• Control Variables: Everything else that must stay the same (e.g., the mass of the pendulum bob, the release angle) so they don’t ruin your results.

2. Methodology and Tools

Your method is your "battle plan." When designing it, you must consider:

• Experimental Techniques: Using the right tools for the job. For example, using a motion sensor (Tool 2) for a falling object instead of a stopwatch to reduce human reaction time.
• Safety and Ethics: Always ask: Is this dangerous? or Does it harm the environment? For example, ensuring heavy weights won't fall on your toes or disposing of batteries correctly.
• Technology: Modern physics uses spreadsheets for data, video analysis for motion, and computer modelling to predict results.

Quick Review: A good design is one where someone else could read your method and repeat your experiment exactly.

Part 2: Inquiry 3 – Concluding

Once you have collected and graphed your data (which you'll learn about in the "Graphing" chapter), you need to say what it actually means. This is the Conclusion.

1. Justifying the Conclusion

You can't just say "the graph goes up." You must justify it using your data.
Example: "The graph of \(V\) against \(I\) is a straight line through the origin, which justifies the conclusion that potential difference is directly proportional to current, as predicted by Ohm’s Law."

2. Comparing Outcomes with "Accepted Science"

Physics is a community! We compare our results to the accepted values found in textbooks or the Data Booklet.
If you calculate the acceleration due to gravity as \(g = 9.5 \text{ m s}^{-2}\), you should mention that the accepted value is approximately \(9.81 \text{ m s}^{-2}\).

3. The Power of the Gradient

In many investigations, the "answer" is hidden in the gradient (slope) of your line of best fit.
If you plot \(y = mx + c\), and your physics equation is \(v = u + at\), then the gradient \(m\) represents the acceleration \(a\).

Key Takeaway: A conclusion isn't just an opinion; it is a claim backed up by mathematical evidence from your data.

Part 3: Inquiry 3 – Evaluating

This is where you become a critic of your own work. No experiment is perfect. Evaluation is about identifying why your result isn't perfect and how to fix it.

1. Random vs. Systematic Errors

Understanding the difference between these two is vital for your exams!

2. Discussing Uncertainties

Every measurement has an uncertainty (e.g., \(\pm 0.1 \text{ cm}\)). In your evaluation, you must discuss if these uncertainties were small enough to trust your conclusion. If your error bars are huge, your conclusion might be "weak."

3. Evaluating Limitations and Assumptions

In Physics, we often simplify the world. We might assume air resistance is negligible or that a string has no mass.
Evaluation Step: Ask yourself, "Was that assumption realistic?" If you were dropping a feather, assuming no air resistance is a major limitation!

Did you know? Identifying a mistake in your experiment isn't "failing." In science, explaining why an error happened is just as important as getting the "right" answer!

Common Mistakes to Avoid

• Confusing Precision and Accuracy: Accuracy is how close you are to the "true" value. Precision is how consistent your repeated measurements are. You can be precisely wrong!
• Vague Improvements: Don't just say "be more careful." Suggest a specific technological fix, like "use a data logger with a higher sampling rate to capture rapid changes in temperature."
• Ignoring the Intercept: If your theory says the graph should pass through \((0,0)\) but it doesn't, that is a huge clue that you have a systematic error.

Quick Review Checklist

✓ Did I identify the IV, DV, and Control Variables?
✓ Did I justify my conclusion using the graph and data?
✓ Did I compare my result to the accepted scientific value?
✓ Did I distinguish between random and systematic errors?
✓ Did I suggest specific, realistic improvements for the limitations found?

Mastering these skills won't just help you in the lab—it will help you think like a physicist in every part of the course!