Welcome to IaS1: Designing Scientific Investigations
Have you ever wondered how physicists go from asking a simple question like "Why do parachutes slow people down?" to carrying out a precise laboratory experiment? In science, getting trustworthy answers depends entirely on how well an experiment is designed and prepared.
This chapter explores Ideas about Science (IaS1): the essential toolkit you need to plan valid, accurate, and safe experiments. Don't worry if scientific terminology has felt confusing before; we will break down every concept step-by-step with real-world examples!
---1. Hypotheses, Predictions, and Models
Every great investigation starts with an idea. However, in physics, we have very specific meanings for the words we use to describe these ideas.
Key Terms to Know
• Hypothesis: A tentative, testable explanation for an observed phenomenon.
Example: "Air resistance depends on the surface area of a parachute."
• Prediction: A specific, measurable statement about what will happen in an experiment if the hypothesis is correct.
Example: "If I double the surface area of the parachute, the fall time will increase."
• Model: A representation (which can be physical, mathematical, or computational) used to explain or predict how a system behaves.
Example: Drawing a free-body diagram showing force arrows for weight and air resistance, or using a computer simulation to model air particles hitting the parachute canopy.
Quick Summary: A hypothesis explains why something might happen, while a prediction states what measurable change you expect to see.
---2. Variables and Fair Tests
To test a prediction, we must design a fair test by carefully choosing which variables to change, which to measure, and which to keep constant.
Types of Variables
• Independent Variable: The factor that you deliberately change to see its effect.
Memory trick: Independent = the one I change.
• Dependent Variable: The factor that you measure as an outcome. Its value depends on the independent variable.
Memory trick: Dependent = the Data you record.
• Control Variables: Factors that must be kept constant throughout the experiment to ensure validity (a fair test).
Crucial Exam Tip: Explaining Control Variables
Examiners often ask: "Why must a student keep a specific variable constant?"
Never just say "to make it fair." You must state that changing a control variable would affect the dependent variable, meaning you would not know whether your results were caused by the independent variable or by the uncontrolled factor.
Types of Data and Graphs
The type of data you collect determines how you present it visually:
• Categoric Data: Data that comes in distinct categories, names, or labels (for example, testing different types of metal: copper, iron, aluminium).
Graph to use: Bar chart.
• Continuous Data: Data that can take any numerical value on a continuous scale (for example, length, mass, temperature, or time).
Graph to use: Line graph.
3. Measurement Quality: Precision, Accuracy, and Errors
In physics, words like "accurate" and "precise" are not interchangeable. Understanding the difference is vital for exam success!
Accuracy vs. Precision
• Accuracy: How close a measured value is to the true value.
• Precision: How close repeated measurements are to each other (showing little spread), regardless of whether they hit the true value.
Analogy: Imagine throwing darts at a dartboard bullseye. If all your darts land clustered tightly together in the top-left corner, your throws are precise but inaccurate. If your darts land close to the bullseye, they are accurate.
Resolution
• Resolution: The smallest change in a quantity that an instrument can detect.
Example: A standard 30 cm wooden ruler has millimetre markings, so its resolution is \(1\text{ mm}\). A digital calliper might have a resolution of \(0.01\text{ mm}\).
Watch out: Using a high-resolution timer (e.g. reading to \(0.01\text{ s}\)) does not automatically make your results accurate if human reaction time (around \(0.2\text{ s}\)) introduces errors!
Repeatability vs. Reproducibility
• Repeatability: The original investigator repeats the experiment using the same method and same equipment and obtains the same results.
• Reproducibility: A different person conducts the experiment, or the same person uses different equipment or methods, and still obtains the same results.
Memory trick: Repeat = same person. Reproduce = recreated by someone else.
Calculating Uncertainty
Whenever you take repeat readings, there is always some uncertainty about the true value. For repeated measurements in GCSE Physics, use this exact formula:
\(\text{Uncertainty} = \frac{\text{Range}}{2}\)
Worked Example:
A student records the fall time of a paper spinner three times: \(10.2\text{ s}\), \(10.4\text{ s}\), and \(10.6\text{ s}\).
1. Find the Range: \(\text{Range} = \text{Maximum} - \text{Minimum} = 10.6\text{ s} - 10.2\text{ s} = 0.4\text{ s}\)
2. Calculate Uncertainty: \(\text{Uncertainty} = \frac{0.4\text{ s}}{2} = \pm 0.2\text{ s}\)
The mean time is \(10.4\text{ s}\), so the result is written as \(10.4 \pm 0.2\text{ s}\).
4. Experimental Errors and Safety
Random vs. Systematic Errors
• Random Error: Unpredictable variations that occur during an experiment (such as slight changes in human reaction time or air currents).
How to fix: Take repeat readings and calculate a mean. This reduces the effect of random errors.
• Systematic Error: A consistent, predictable error that shifts all measurements away from the true value by the exact same amount each time (such as looking at a scale from an angle, or equipment that is faulty).
How to fix: Averaging repeats will not remove systematic errors. You must identify the fault in the equipment or technique and correct the method.
• Zero Error: A specific type of systematic error where a measuring device does not read zero when it should (for example, a top-pan balance reading \(0.5\text{ g}\) when empty).
How to correct it: Subtract the zero error value from all subsequent readings!
Hazard vs. Risk vs. Control Measure
Safety planning is an essential part of preparing an investigation:
• Hazard: An object or situation that has the potential to cause harm (e.g. exposed high-voltage wires, hot water, heavy falling masses).
• Risk: The likelihood and severity of harm occurring from that hazard (e.g. risk of electric shock when touching bare wires).
• Control Measure: A specific physical action taken to reduce the risk (e.g. using insulated wires, wearing heat-resistant gloves, or placing a sand tray under falling weights).
Exam tip: Stating "be careful" will score zero marks. Always describe a specific, concrete action!
5. Standard Conventions and Exam Rules
OCR examiners check for specific scientific conventions. Follow these rules to avoid losing easy marks:
Table Formatting
In all results tables, column headers must strictly follow the format: Quantity / unit.
Correct Examples:
• \(Time / s\)
• \(Length / m\)
• \(Potential\ difference / V\)
• \(Current / A\)
Significant Figures and SI Units
• Significant Figures: When you perform calculations, round your final answer to the same number of significant figures as the least precise raw data value you used.
• SI Units: Always convert measurements into standard SI units before doing calculations:
- Mass must be in kilograms (\(\text{kg}\)), not grams.
- Distance/Length must be in metres (\(\text{m}\)), not centimetres or millimetres.
- Time must be in seconds (\(\text{s}\)), not minutes.
Common Pitfalls to Avoid
• Never use the word "amount": Use precise terms such as mass, volume, length, or current.
• Do not confuse resolution with accuracy: A digital meter showing four decimal places can still be completely inaccurate if it has a zero error.
• Remember to subtract zero error: If a spring balance reads \(0.2\text{ N}\) with no weight attached, subtract \(0.2\text{ N}\) from every reading taken.
Quick Chapter Review
• Hypothesis: Explains why a phenomenon occurs; Prediction: States what will be measured.
• Independent: What you change; Dependent: What you measure; Control: What you keep constant to ensure a valid test.
• Categoric data: Use a bar chart; Continuous data: Use a line graph.
• Accuracy: Closeness to true value; Precision: Closeness of repeats to each other.
• Repeatability: Same person/method; Reproducibility: Different person/method.
• Uncertainty: \(\frac{\text{Range}}{2}\).
• Random errors: Reduced by taking the mean of repeats; Systematic/Zero errors: Corrected by fixing the instrument or subtracting the offset.