The Internal Assessment in IB Physics is a scientific investigation that you design, carry out and report individually. It is one of the few parts of the course where you choose the question, and it contributes to your final grade. This guide explains what the scientific investigation is, how to choose a strong investigation, how to design and report it, and how to avoid the most common mistakes that cost marks. It applies to the current IB Physics course at both Standard and Higher Level.
A note on the current course. This guide uses the terminology of the current IB Physics course. The internal assessment is called the scientific investigation. Confirm the exact assessment details, including its weighting, duration and mark allocation, for your specific course and level against the official IB subject brief, and follow your school's internal deadlines and guidance.
What Is the IB Physics Scientific Investigation?
The scientific investigation is an individual piece of practical work in which you investigate a research question of your choice using physics at an appropriate level for your course. It is not a recipe experiment and not a research essay about physics. The investigation is meant to show that you can do physics: frame a question, design a method, collect and process data, interpret results, and evaluate the outcome.
The investigation is internally marked by your teacher against the IB assessment criteria and then externally moderated. The criteria reward clear research question design, appropriate methodology, careful data collection and processing, sound analysis, genuine evaluation, and clear scientific communication, not just a correct final answer. A strong investigation reads like a coherent piece of scientific work produced by a student who genuinely wanted to answer the question.
Choosing a Good Physics Investigation
Investigation choice is where most investigations succeed or fail before they begin. A strong investigation has several characteristics:
- A focused research question. A single, clearly stated question that can be investigated practically. "How does X affect Y?" is a workable frame; "the physics of sport" is not.
- Measurable variables. An independent variable you can vary and a dependent variable you can measure with available equipment.
- Feasible data collection. An experiment you can actually perform with the time, apparatus and precision available in a school setting.
- Appropriate physics. The investigation should involve physics connected to your course (SL or HL) and developed beyond the most routine application.
- Sufficient scope for analysis. Enough variation in the independent variable, and enough repeats, to support a meaningful relationship and evaluation.
- Realistic experimental design. A method that controls the right variables and produces data precise enough to draw a conclusion.
Avoid the temptation to pick a topic because it "looks impressive." The best investigations come from questions you genuinely want to answer and can investigate properly. There is no such thing as a guaranteed-7 idea; the marks come from how well you execute and report the investigation, not from the topic itself.
Choosing Variables and Designing the Experiment
A clean experimental design rests on clearly identified variables:
- Independent variable. The variable you deliberately change. It should be measurable and span a sensible range.
- Dependent variable. The variable you measure in response. It should be measurable with adequate precision for the relationship you hope to detect.
- Controlled variables. The variables you keep constant so that any change in the dependent variable can be attributed to the independent variable. Weak control of variables is one of the most common reasons a conclusion becomes unreliable.
- Measurement strategy. Choose instruments that give enough precision, and decide in advance how many repeats and how many values of the independent variable you will take.
- Repeatability. Repeating measurements and reporting a mean improves the reliability of your data and lets you discuss it in the evaluation.
- Practical feasibility. A method that actually works in the time and with the equipment available. A clever idea you cannot carry out produces no data.
Before you commit to a method, do a quick trial. A short pilot run often reveals practical problems, equipment limits, timing issues, uncontrolled variables, that are easy to fix early and expensive to fix late.
Collecting and Processing Data
Good data collection and processing are systematic, not improvised:
- Appropriate measurements. Take enough values across a sensible range of the independent variable, and repeat each measurement so you can assess scatter.
- Tables. Record raw data in clear, labelled tables with units in the headers, not in every cell.
- Units. Use correct SI units throughout, and keep them consistent.
- Graphs. Plot the dependent variable against the independent variable. A well-chosen graph is the clearest way to reveal a relationship.
- Relationships. Look for the pattern in your data, linear, proportional, inverse, or otherwise, and connect it to the physics you expect.
- Uncertainty where relevant. Estimate the uncertainty in your measurements and propagate it sensibly through any processing. Treat uncertainty as part of the evidence, not an afterthought.
- Appropriate processing. Process data only where it helps reveal the relationship. Unexplained processing adds nothing.
Every step of processing should be explained. If you compute a derived quantity, state how and why. A column of processed numbers with no explanation is hard to follow and easy to mark down.
Analysis and Interpretation
Analysis is the move from data to physical meaning. It is not the same as describing the data. A strong analysis goes: data, relationship, physical interpretation.
Start by identifying the relationship in your processed data, what the graph shows. Then interpret that relationship in physical terms: does it match what the underlying physics predicts? If your data suggests a proportional relationship, say so and connect it to the theory. If the relationship deviates, explain why that might be. Avoid generic lab-report phrasing such as "the graph goes up"; say what the shape means for the physics you are investigating.
Where you have used a linearised graph, for example plotting a derived quantity to obtain a straight line, explain the reasoning: what the gradient and intercept represent, and what they tell you about the system. The interpretation is where much of the physics credit is earned, because it shows you understand what your data means, not just how to plot it.
Evaluation
Evaluation is where you judge the quality of your evidence and your conclusion. A strong evaluation discusses:
- Limitations. What constrained your investigation: equipment, precision, time, uncontrolled variables.
- Sources of uncertainty and error. Where the main uncertainties came from, and how large they are relative to the effect you measured.
- Impact on results. How those limitations and uncertainties actually affected your data and conclusion, not just that they existed.
- Realistic improvements. Specific, practical changes that would reduce the uncertainty or address the limitation. "Use better equipment" is weak; "use a motion sensor instead of a stopwatch to reduce reaction-time error" is specific and useful.
- Whether the conclusion is supported. An honest statement of how far your evidence supports the conclusion you drew. A conclusion stronger than the evidence is a common and costly mistake.
Genuine evaluation is reflective and specific. A short paragraph listing generic "human error" earns little because it shows no understanding of what actually limited your investigation.
Common Physics IA Mistakes
The same mistakes appear in weak investigations year after year:
- Research question too broad. A question that cannot be investigated in the time and with the equipment available.
- Experiment not feasible. A method that looks clever but cannot be carried out, producing little or no usable data.
- Insufficient data. Too few values of the independent variable, or no repeats, leaving the relationship unsupported.
- Weak control of variables. Uncontrolled variables that make it impossible to attribute changes in the dependent variable.
- Unexplained processing. Derived quantities or calculations with no stated method or justification.
- Graphs without interpretation. A plotted graph that is never connected to the physics or the conclusion.
- Generic evaluation. "Human error" and "use better equipment" with no specifics.
- Conclusions stronger than the evidence. Claiming a relationship is confirmed when the data and uncertainty do not support it.
How HK Study Hub Can Help
Legitimate tutoring support for the scientific investigation focuses on helping you produce better work, not on producing it for you. A tutor can help with refining an idea into a focused research question, understanding the underlying physics, thinking through experimental design and variables, planning data analysis and uncertainty, interpreting results, and giving feedback on clarity and structure. The student remains responsible for producing and submitting their own assessed work. If you would like support, see our IB Physics tutoring in Hong Kong.
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IB Physics IA FAQs
How do I choose a topic? Start from a question you genuinely want to answer, check it has measurable variables and feasible data collection, and make sure the physics is appropriate to your course. Avoid topics chosen only because they look impressive.
What makes a good research question? A single, focused, clearly stated question with an independent and dependent variable you can measure. "How does X affect Y?" is a workable frame.
How many variables do I need to control? All variables that could affect the dependent variable apart from the independent one. Identifying and controlling them is part of the method, and weak control is a common reason conclusions become unreliable.
Do I have to include uncertainty? Estimating and reporting uncertainty is part of good scientific practice and is expected where relevant. Treat it as part of the evidence, not an add-on.
How should I analyse my data? Move from data to relationship to physical interpretation. Identify the pattern, connect it to the physics, and explain what the graph means.
What goes in the evaluation? Specific limitations, the main sources of uncertainty, their impact on your results, realistic improvements, and an honest judgement of whether your evidence supports your conclusion.
Can a tutor review my IA? A tutor can give feedback on clarity, structure, experimental reasoning and interpretation. They cannot write or rewrite assessed work for you.
Can a tutor write any part of it? No. The investigation must be your own work. Tutoring that crosses into writing the investigation for you is academic misconduct and risks your result.
For more IB Physics resources, browse the IB Physics resource hub, see our data analysis guide and revision guide, or explore all IB resources and IB tutoring in Hong Kong.