Experimental Programme

Exploring

The first step in any scientific investigation is to ask a clear question and come up with a hypothesis — a prediction based on what you already know about physics. For example:

Research Question: How does the angle of a ramp affect the acceleration of a rolling object?

Hypothesis: As the angle of the ramp increases, the acceleration of the object will increase due to a larger component of gravitational force along the ramp.

To support this hypothesis, you’d look at reliable sources — physics textbooks, academic papers, or websites like HyperPhysics or Khan Academy. Nowadays, you can also use computer simulations like PhET Interactive Simulations to test ideas before doing a physical experiment. This helps you explore complex ideas in a visual and interactive way.

At this stage, it’s important to think independently — maybe you decide to compare rolling vs. sliding objects or try different materials. That kind of initiative shows strong insight and creativity.

Designing

Next, you plan how you're going to test your idea. This includes deciding on:

• Independent variable: the thing you change (e.g. ramp angle),

• Dependent variable: what you measure (e.g. acceleration),

• Control variables: things you keep the same (e.g. object mass, surface material, ramp length).

Example Setup: You use a motion sensor or video analysis (like Tracker software) to measure acceleration as you change the angle of a wooden ramp.

Designing also means deciding how many measurements to take — for example, testing 5 different angles and repeating each 3 times for reliability. You might also do a pilot test — a trial run — to check if your method works or if you need adjustments (e.g. maybe the object keeps slipping, so you switch to a rubber-coated ramp).

Creativity is key here: maybe you add a simulation of motion in GeoGebra to compare with real data.

Collecting Data 

This is when you start taking actual measurements. You might use tools like:

• Light gates or motion sensors to measure speed and time,

• Video analysis (using your phone + Tracker) for rolling motion,

• Simulations to model theoretical acceleration.

For example: You record a video of a ball rolling down the ramp and analyze its position frame-by-frame to calculate acceleration.

You’ll also take qualitative observations: "The object slipped at angles above 45°" or "It wobbled when released too quickly."

Don't forget to include uncertainties — for example, if your stopwatch has a precision of ±0.01 s, or if position data from Tracker is only accurate to ±2 cm.

Processing Data

After collecting data, you process it to reveal patterns. This includes:

• Calculating averages,

• Graphing data (e.g. angle vs. acceleration),

• Propagating uncertainties using appropriate rules (e.g. adding or multiplying with uncertainties),

• Using spreadsheets like Excel or Google Sheets to make calculations efficient and avoid mistakes.

Example: You calculate the acceleration from velocity-time graphs and show error bars on a graph of angle vs. acceleration. Make sure you explain how you did the processing: Did you use the equation a=Δv/Δt​? Did you estimate the gradient using two well-chosen points? 

Concluding

Now you interpret your results. What do the numbers and graphs tell you?

Conclusion Example: As predicted, acceleration increased with ramp angle. The data follows a sine curve trend, which agrees with the physics equation a=gsin⁡(θ).

Link your results back to the original hypothesis and also to accepted theory. Discuss if the data fits the model, and mention any outliers or inconsistencies.

Also, acknowledge uncertainty: Maybe your angle measurements weren’t precise, or friction affected results — these factors can influence how confident you are in the conclusion.

Evaluating

Here you critically reflect on your experiment. Consider:

• Were your hypothesis and reasoning sound?

• Did any systematic errors (like miscalibrated sensors) affect results?

• Were there random errors (like reaction time or wobbling objects)?

• What assumptions did you make (e.g. neglecting air resistance)?

Example: "We assumed friction was negligible, but the ball slowed down more than expected. This could have lowered measured acceleration."

Then, suggest realistic improvements:

• Use smoother ramps or objects to reduce friction,

• Add more data points,

• Use higher-resolution sensors or more accurate timing.

These steps help ensure future investigations are more accurate and meaningful.