IB Physics Internal Assessment Guide

The IB Physics Internal Assessment (IA) is your opportunity to demonstrate the practical, analytical, and critical thinking skills that define a scientist. More than just a lab report, it is a structured, independent investigation where you design an experiment, collect data, and evaluate your process. Mastering the IA is crucial, as it constitutes a significant portion of your final IB Physics grade and is a tangible showcase of your scientific understanding.

From a Spark to a Research Question

The foundation of a successful IA is a well-defined and original research question. This is not a topic like "electricity" or "pendulums," but a specific, measurable, and manageable inquiry into a relationship between two variables. The best questions often stem from personal curiosity about a phenomenon you’ve observed. For example, instead of "I will study springs," a strong research question is: "How does the length of a cantilever spring affect its period of oscillation when a fixed mass is attached at its end?"

Your question must identify the independent variable (the one you change, e.g., length), the dependent variable (the one you measure, e.g., period), and the controlled variables (the ones you keep constant to ensure a fair test, e.g., mass, cross-sectional area of the spring). The scope must be narrow enough to be completed with school-level equipment but deep enough to allow for meaningful data collection and analysis. A vague question leads to a vague investigation.

Designing a Method for Reliability

Once your question is set, you must design a method that produces valid and reliable data. Validity means your experiment actually tests your research question. If you’re investigating the effect of length on period, your method must allow you to cleanly change only the length while accurately measuring the period.

Reliability is achieved through repetition and control. You should clearly describe how you will measure each variable with appropriate apparatus (e.g., a laser gate for timing, a vernier caliper for length). Explain the step-by-step procedure, including how you will vary the independent variable across a suitable range (typically 5-8 different values) and how many repeat trials you will perform for each (a minimum of 5 is strongly advised to calculate uncertainty). A detailed, labeled diagram of your setup is essential. This section must be replicable; another student should be able to follow your instructions exactly.

Data Collection and Processing with Uncertainties

Raw data must be presented in clear, well-formatted tables. Every measured value must be accompanied by its absolute uncertainty. For analogue instruments like a ruler, this is typically ± half the smallest division. For digital instruments, it is often ± the smallest digit. For repeated measurements, the uncertainty is half the range of your readings or the standard deviation, whichever is larger.

Data processing transforms raw measurements into the quantities you need to analyze. This often involves calculations. For each processed value, you must propagate the uncertainties. If you calculate kinetic energy $E_k=\frac{1}{2}m{v}^2$, you need to determine the uncertainty in $E_k$ based on the uncertainties in mass $m$ and velocity $v$. Show one sample calculation in full.

The heart of your analysis is the graphical representation. Plot the processed data, with the independent variable on the x-axis and the dependent variable on the y-axis. Plot error bars for both variables using your calculated uncertainties. Then, determine the mathematical relationship. If you suspect a linear relationship $y=mx+c$, use a line of best fit. Do not simply connect the dots. Use software or a manual method to find the gradient $m$ and y-intercept $c$ of the best-fit line. For non-linear relationships (e.g., $T\propto \sqrt{L}$), you must linearize the data by manipulating the variables (e.g., plotting $T$ against $\sqrt{L}$).

Evaluation and Discussion: The Mark of a Scientist

This section separates adequate reports from excellent ones. Begin by interpreting your results. What does the gradient of your graph physically represent? Does it match the theoretical value? Calculate a percentage difference and discuss possible reasons for any discrepancy.

Then, conduct a rigorous evaluation of your investigation’s strengths and weaknesses. Discuss the systematic errors (errors that shift all data in one direction) and random errors (unpredictable variations) in your method. For a systematic error, was the ruler zeroed correctly? For random error, were air currents affecting a pendulum? Crucially, do not just list errors—quantify their impact and suggest specific, realistic improvements. For example: "Friction at the pivot was a systematic error, likely causing the measured period to be 2-3% higher than the theoretical value. A future investigation could use a low-friction bearing and measure the damping of oscillations to estimate the energy loss directly."

Compare the magnitude of your experimental uncertainties to the discrepancy from theory. Was your method precise enough to test the relationship? Finally, propose a meaningful extension to your research question that would build logically on your findings.

Writing the Structured Report

Your written report must adhere to the IB structure and meet all criteria clearly. While formats can vary, a logical flow is:

1. Introduction: Contextualize your research question with relevant physics theory. State your question clearly at the end.
2. Methodology: Describe the procedure, variables, apparatus, and safety considerations. Include diagrams.
3. Data Collection: Present raw data tables with uncertainties and observations.
4. Data Processing: Show calculations, processed data tables, and graphs with error bars and best-fit lines.
5. Conclusion: Summarize the relationship found, using your graph's gradient/intercept as numerical evidence. State whether it supports the initial hypothesis.
6. Evaluation: Discuss weaknesses, errors, and improvements as detailed above.

Throughout, use clear, concise, and objective scientific language. Each section should help the examiner easily identify how you have addressed the IA assessment criteria: Personal Engagement, Exploration, Analysis, Evaluation, and Communication.

Common Pitfalls

The Vague Question: Starting with "To find the acceleration due to gravity" is not a research question. Correction: Frame it as an investigation of a relationship: "How does the length of a simple pendulum affect its period, and can this relationship be used to determine a local value for gravitational acceleration $g$?"

Ignoring Uncertainties: Presenting data without associated uncertainties or error bars severely limits your analysis score. Correction: Record the precision of every instrument used. Calculate absolute uncertainties for raw data and propagate them through all calculations. Always plot error bars.

Forcing a Linear Fit: Plotting clearly curved data and drawing a straight line through it is a critical error. Correction: Analyze the expected theoretical relationship from physics principles. If it is of the form $y=k{x}^n$, linearize your data by plotting $\log y$ against $\log x$. The gradient of this new graph will give you the power $n$.

Generic Evaluation: Stating "human error" or "equipment error" is uninformative. Correction: Identify specific, physics-based sources of error (e.g., "assumed no air resistance," "assumed the spring was massless"). Explain how each error affected your results (systematically high/low) and propose an actionable improvement that directly addresses it.

Summary

• Your research question must be focused, measurable, and investigate the relationship between a clear independent and dependent variable.
• A valid and reliable method requires detailed planning, identification of controlled variables, and sufficient repeat trials to establish uncertainty.
• Data processing is incomplete without proper uncertainty propagation and graphical analysis, including error bars and a justified line or curve of best fit.
• A high-scoring evaluation quantifies the impact of specific systematic and random errors and links precise, practical improvements directly to them.
• The final report must be a well-structured, clearly communicated document that explicitly guides the examiner to evidence for each of the IB assessment criteria.