General Advice

Research design

This criterion assesses the extent to which the student effectively communicates the methodology (purpose and practice) used to address the research question.

In this "Research design" criterion there are three strands. The first strand concerns the research question. The second and third strands are the next two items in the markband. Each strand repeats in each markband, incrementally differentiated each time. In "Research design" the three strands appear three times each, in markbands 1-2, 3-4, 5-6.

Awarding zero for a criterion

It is rare that a student's report should be awarded a zero, but there are specific circumstances in which this is appropriate. In the case of an incomplete report if there is no evidence at all for a criterion, a zero is awarded. If there is some evidence of some effort to address a criterion, the work should only be awarded zero if the response is incomprehensible or totally irrelevant to the criterion. If one strand of a criterion scores a zero when the other strands do not—if a strand is not addressed at all, or the student's work does not reach the required standard for scoring in that strand, the appropriate score is zero. However, if the other strands are found to match the higher markbands, the overall mark for the criterion should reflect the student's achievement, that is, not over-penalizing the zero-scoring strand.

Investigations not focused entirely on physics

The internal assessment is not an opportunity for an interdisciplinary study; the focus should be on physics.

Collecting sufficient data

Designing the collection of sufficient data

Students should be creative or demonstrate initiative in the design, implementation or presentation of their investigation. It is not expected that every investigation will generate a lot of data. The data collected should be commensurate with the 10 hours' work required for the scientific investigation overall. Student guidance is necessary, especially at the outset of the scientific investigation.

There is no single standard for determining that data collected in an investigation is insufficient: some physics investigations will generate data more quickly than others. The amount of data collected that addresses a methodology will be determined by the nature of the investigation and the time available. The number of repeats should be selected with a clear rationale.

For example, an investigation to determine the coefficient of restitution of a bouncing ball will usually generate data more quickly than an investigation into heat loss.

Some limitations may only become apparent during data processing. It is strongly recommended that the student considers the appropriateness of data as they are being collected. Carrying out rough processing while collecting data may help the student identify issues. This allows them to modify the range, interval or frequencies as well as collect additional data.

The report should be an account of what happened, and the outcomes may not be those predicted. The report should include details of problems encountered when collecting data. It should also describe issues faced by the student during their trials and how the student responded to them. One possibility is that a potentially feasible investigation fails to provide sufficient data, through no fault or inexperience of the student. The data may be rough and possibly inconclusive. The student should be adaptable, and express this in their account of the method.

Sufficient data are necessary for adequate processing to be evidenced. Interpreting the results and arriving at a conclusion will require care and attention if the results are inconclusive.

The impact of the amount of data collected on other criteria

Insufficient data and poor consideration of uncertainties may impact students' marks for all the criteria. The amount of data collected will impact the type of processing that can be done.

If, through no fault of the student, insufficient data are collected, the only viable processing may be to calculate the mean and the range. If the processing carried out is commensurate with the level expected to address the research question, the work could achieve the highest marks available.

Insufficient data will impact on the conclusion and the evaluation of the investigation. It is important to look for evidence in the report that the student is aware that the amount of data is limiting the conclusion. Note too that if the data are limited the processing will also be limited, and the interpretation will be impacted too. The teacher should counsel the student to follow a process that will be productive with the time and materials available. If there is no good reason why more data could not have been collected, the mark for data analysis could be impacted.

Propagation of uncertainties is expected, rather than the use of standard deviation or statistical analysis.

Not all data processing is equally complex. Students who have extensive data, or whose data require complex processing, may be more likely to make mistakes. In the case of large data sets, carefully consider what the student has done, rather than penalize minor errors in the processing. Major errors in processing may influence interpretation and the conclusion.

Using databases, simulations and models

For work involving databases, simulations and models, the same design rules apply. The source of the data needs to be identified clearly, its reliability established, and its sufficiency and relevance to answering the research question considered. Databases and simulations are freely accessible online.

When using secondary data sources, more independent variables can be included, because sufficient data can be collected rapidly.

The student will need to explain how the data sampling is controlled and how it is extracted or filtered. They should explain their selection and the decisions taken for extracting data, and consider using a series of screenshots to illustrate the method. When using databases and simulations, the student must include screenshots or pictures to illustrate the methodology.

Investigations based on tables found in published articles are rarely suitable, because the authors have often already made decisions that the students themselves should make.

Repetitions that result in the same value each time receive minimal credit. In this case, the student can use additional simulations or databases to gather sufficient data to answer the research question. The student must explain how and why the specific simulation was used, and the methodology for collecting data.

Investigations based on computational analysis should include tools that calculate properties accurately, rather than those limited to visual representations.

Communication style and report length

The structure of the internal assessment report is the student's responsibility. The IB offers no guidelines here, except that the report should be clear, concise and focused, and demonstrate relevant scientific skills.

A cover page and a table of contents are often distractions and should not be included. A clear and informative title reflecting the research question and a first paragraph should inform the reader about the investigation.

There is no fixed style for presenting the method. Both prose and recipe style are acceptable. This is a physics investigation, not an assessment of language skills. The use of the passive voice or a personal style should not impact the marks given. Neither should errors of expression, spelling and grammar, unless these result in ambiguity, contradictions, or amount to incomprehensible content. The structure, scientific relevance and conciseness of the report are more important than the language used. This is particularly important considering that many students are not working in their preferred language.

Assessment is always based on evidence from the student's report, and this evidence needs to be clearly communicated in scientific terms. Effective communication is not a criterion on its own; it is an essential part of all four criteria.

• Effective communication is explicit in the research design criterion. The student needs to communicate the methodology (the purpose and practice) and the context of their investigation.
• Effective communication is an aspect of the data analysis criterion, where the recording and processing of data should be clear, precise and accurate in relation to the research question.
• Effective communication is also implicit in the conclusion and evaluation criteria, where an answer to the research question must be justified and where evidence of an evaluation needs to be expressed.

The report should be a maximum of 3,000 words. The word count does not include data tables, sketches, graphs, headings, references or bibliographies. Where a large amount of data has been collected, only a sample of the data should be included. If a report is clear, concise and focused, 3,000 words (maximum) will be more than adequate. If the report exceeds this limit, examiners are not compelled to read further.

Citations, bibliographies and academic integrity

If the student is quoting broadly accepted facts or theories, citations are generally not needed. However, if a specific fact is quoted, a citation would be expected. For example, students do not need a citation when stating Newton's second law of motion, but the value of the coefficient of static friction between two surfaces would require a citation.

Citations can be in-text, in footnotes on each page, in endnotes, or written as references in a bibliography. The citations should allow sources to be traced, for example the URL and retrieval dates for online sources. They should be limited to sources that have been used in the investigation, either for ideas, content quoted or images copied. The style of citation is up to the student, but they should follow a clear and consistent method of referencing. The bibliography can be used to record the full reference details.

Academic integrity is important to IB educational philosophy and, indeed, to any academic pursuit. Honesty is the hallmark of scientific inquiry. When writing their reports, the student must clearly distinguish between their words or ideas and those of others.

If a teacher believes some of a report's content may have been taken from a source without adequate citation, this may be a case of malpractice. The teacher must discuss this with the student, to clarify how and why the content came to be presented in the report.

If an examiner is not satisfied that the report is the student's own work, the IB will instigate an inquiry on suspected malpractice.

For further guidance, refer to the IB's Academic integrity policy and Effective citing and referencing.

Appendices

All information relevant to the investigation must be presented within the report. Appendices will not be read by the examiner. The only appendices permissible and sometimes necessary are consent forms for students participating in data collection.

IB Physics Internal Assessment (IA): Research Design

1. Overview & General Requirements

• The Task: A single, individual experimental report where students design an investigation, collect and analyze data, and evaluate their findings.
• Time Allocation: Designed to take approximately 10 hours of class/lab time. The method must collect sufficient data within this realistic timeframe (including trialling and redesign).
• Word Count: Strict maximum of 3,000 words. Examiners are instructed to stop reading exactly at the 3,000-word mark.
• Excluded from Word Count: Charts, graphs, data tables, equations, formulas, sample calculations, diagrams and their labels, citations, references, bibliography, headers, and titles.
• Page Limit: No strict page limit, but concise scientific communication is expected. A well-formatted 3,000-word report with tables and graphs averages 12-15 pages.
• Weighting & Levels: Counts for 20% of the final IB Diploma grade. Assessment criteria are exactly the same for both Standard Level (SL) and Higher Level (HL). All students can explore topics outside the syllabus.
• Assessment Criteria: 4 criteria total (24 marks). Research Design is worth 6 marks.

2. First Page Formatting

• Title of the Investigation: Clear, descriptive, outlines the scope.
• IB Candidate Session Number: Alphanumeric (e.g., xyz123). CRUCIAL: The student's actual name and the school's name must NOT appear anywhere. Ensure photos of setups do not show textbooks or labels with names.
• Subject and Level: State "Physics SL" or "Physics HL".
• Assessment Session: e.g., May 2026.
• Word Count: Exact word count of the IA.
• Research Question (Recommended): Place the explicit RQ on the first page immediately below the title to set clear focus.

3. Research Question (RQ) and Context

• Uniqueness: The RQ must be unique to the student.
• Variables: Must include an independent and dependent variable (or derived dependent variable) that are quantifiable. Focus almost exclusively on exactly one independent and one dependent variable.
• Quantitative Nature: Variables must be quantitative (measurable numerical amounts/continuous, or distinct integer counts/discrete). Non-quantitative/categorical variables (like type of metal or color) cannot be mathematically operated on to linearize curves and should be avoided as the primary independent variable.
• Focused Question Requirements:
  • Pinpoint Scientific Vocabulary: Replace everyday words (e.g., "bouncy", "bending of light") with measurable physics terminology (e.g., "coefficient of restitution", "lateral displacement").
  • Precisely Defined Physical System: Specify the exact object and static parameters (e.g., instead of "a wire", use "a 30 SWG constantan wire").
  • Clear Boundaries (Scope): Imply a manageable scope for standard high school equipment within 10 hours. Adding the specific range of the independent variable provides focus.
• Background Context & Theory: The context should help understand the specifics of the RQ and its implementation. General overviews (e.g., broad summary of electric circuits) will not score high. Focus reading on the exact RQ. The context must establish the link between variables (e.g., referencing Faraday's Law or damping coefficients).
• Hypothesis (Optional): Not obligatory, but helps set the RQ in context. If a hypothesis is included, it MUST be addressed in the conclusion.

4. Recommended Report Structure

1. Title & Research Question: Clear, descriptive, and defining the variables.
2. Introduction & Scientific Context: Brief background theory and hypothesis.
3. Methodology: Apparatus list (with precision), Variables list, Safety considerations, Step-by-step procedure, and annotated diagram.
4. Data Collection (Raw Data): Formatted tables with units, uncertainties, and qualitative observations.
5. Data Processing & Analysis: Sample calculations, uncertainty propagation, processed data tables.
6. Graphical Presentation: Main graph(s) with max/min lines of best fit to determine gradient uncertainty.
7. Conclusion: Justifying results against background theory.
8. Evaluation: Limitations, impact on data, and realistic improvements.

5. Methodology and Experimental Design

• Reproducibility: The methodology must read like a precise manual that another physicist could reproduce without asking questions.
• Variables & Range:
  • Independent Variable: Define the range and increments. Minimum of 5 data points across a broad enough range to reveal a trend (4 accepted in exceptional cases).
  • Dependent Variable: Detail exactly HOW it is measured, using standard protocols where appropriate (cite them). Provide rationale for sampling rate to ensure meaningful, non-excessive data.
  • Controlled/Confounding Variables: Listing is not enough. Explicitly state the physical method used to keep each variable constant (e.g., "monitored using a digital thermometer and kept at 22.0 ± 0.5 °C by..."). Identify variables that cannot be controlled but must be monitored.
• Apparatus and Setup:
  • Instrument Precision: List every piece of measuring equipment with its absolute uncertainty (e.g., Digital caliper, ±0.01 mm) and sizes.
  • Annotated Diagram: A clear, labeled 2D schematic of the setup is superior to lengthy paragraphs describing placement. Use photographs if they add value. Avoid superfluous images (e.g., a standalone picture of a stopwatch).
• Procedural Reproducibility:
  • Data Collection Steps: Outline the step-by-step sequence in a logical order without unnecessary repetition. Ensure correct scientific terms (spelling not penalized if unambiguous).
  • Trials & Repeats: Explicitly state the number of trials. A minimum of 3 trials is standard for calculating mean and random error, though 5 is preferable for robust error bars. If the data reflects repeats, lack of explicit mention won't necessarily be penalized, but it is expected practice.
  • Calibration: Note steps taken to calibrate sensors, zero balances, or check for zero errors.
  • Preliminary Trial: A preliminary or trial investigation should be described and assessed to refine range, intervals, and sampling rate.

6. Risk Assessment (Safety, Ethical, & Environmental)

• Requirement: A brief risk assessment is explicitly required, checking for safe handling of chemicals/equipment, application of IB guidelines, judicious consumption of materials, and proper waste disposal.
• Mitigation: Identify specific hazards (e.g., high currents, lasers, heavy masses) and the practical mitigation steps taken. Stating a risk is not enough; it must be addressed.
• Zero Risk Scenarios: If an investigation genuinely has no safety, ethical, or environmental considerations (e.g., certain database or simulation work), the student MUST include a statement to this effect.
• Ethics in Databases: Ethical issues can arise in the use of databases; raising them is to the student's credit.
• Unsafe Experiments: Setups constituting a severe risk will be referred to the IB, and the school may be contacted.

IB Physics Internal Assessment: Data Analysis Criteria

Overview and Weighting

The Data Analysis criterion is worth 6 marks, which accounts for 25% of your total Internal Assessment (IA) grade. This criterion evaluates your ability to record, process, and present data in ways that effectively address your research question.

Requirements for Top Marks (5-6 Level)

To achieve the highest marks, your report must demonstrate the following:

• Communication: The recording and processing of data must be both clear (easily understood) and precise (strictly following conventions).
• Uncertainties: The report must show evidence of an appropriate consideration of uncertainties.
• Processing: The processing of data relevant to the research question must be carried out appropriately and accurately.

1. Data Collection and Tables

• Sufficient Data: Collect enough relevant data to address your research question.
• Table Formatting: Provide appropriate titles and reference your tables in the text (e.g., "Figure 1").
• Column Headers: Include concise column headings with units and uncertainties. Standard formatting uses a solidus to separate the quantity and unit (e.g., d / cm) or parentheses.
• Qualitative Observations: Include labeled images, drawings, or written descriptions accompanying your raw data where applicable.

2. Units and Decimal Places

• SI/Metric Units: Use standard SI or metric units. Non-decimal units (like inches or degrees Fahrenheit) are not appropriate. Non-SI units (like eV or ly) are only acceptable if justified by the scientific context.
• Consistency: Maintain a correct and consistent number of decimal places based on your instrument's degree of precision. Match the decimal places of raw data, processed values, and uncertainties.

3. Uncertainties and Errors

• Measurement Uncertainty: Justify the size of your uncertainty based on the experiment. The minimum is the instrument's least count, but realistic uncertainties (such as human reaction time) or variations in repeated trials are often larger.
• Repeated Measurements: Determine the uncertainty of a mean by calculating one-half of the range between the maximum and minimum values.
• Significant Figures: Most uncertainties should be expressed to a single significant digit, though two are acceptable if the first digit is a "1".
• Propagation: Use the non-statistical rules in the DP Physics data booklet for propagating uncertainties through calculations.

4. Processing Data and Graphing

• Methodology: Show your processing clearly. Means, standard deviations, or percentages are sufficient for simple processing. For complex spreadsheets, include a screenshot with the formula used. Provide worked examples for unorthodox calculations.
• Axes and Labels: Label both axes with full quantity names or symbols, and include units. Ensure the scale and proportions are adequate.
• Trend Lines: Draw appropriate best-fit lines or curves guided by theory. Do not simply connect data points. Scatter should be roughly equal above and below the line. Do not force the line through the (0,0) origin unless theoretically sound. Extrapolating beyond your maximum value can provide additional evidence.
• Uncertainty Bars and Min/Max Lines: Include uncertainty bars. Estimate minimum and maximum trend lines by eye considering all uncertainty bars—do not merely connect the first and last data points.
• Linearization: Linearization is not mandatory, but if used, minimum and maximum lines can help determine uncertainty in the gradient and intercept.

5. Handling Outliers

• Do Not Delete Erroneously: Do not systematically remove data points to make results fit a theory. Outliers are real measurements.
• Justification Required: If you exclude data, you must provide a strong scientific justification. Remeasure if human error or methodological flaws are suspected.
• Limit Deletion: Never reject two or more data points as it compromises the dataset's integrity. Consider presenting the analysis with and without the outlier to reveal its impact.

IB Physics IA Guidelines: The Conclusion

Overview of the Conclusion Criterion

The Conclusion criterion is worth a maximum of 6 marks, which accounts for 25% of your total Internal Assessment (IA) grade. It evaluates how successfully you answer your research question by drawing on your data analysis and comparing it to accepted scientific context.

1. Addressing the Research Question and Trends

Your conclusion must directly address the research question and be fully consistent with the data you have presented.

• Trend Analysis: Explain the trend line using correct mathematical terminology. Identify if the relationship is "linear" (state positive or negative gradients), "directly proportional", "inversely proportional", or "exponential". Mention features like minima, maxima, or intercepts.
• Avoid Bias: Ensure your conclusion is genuinely supported by the data. If your data is inconclusive, lacks clear patterns, or only partially supports a hypothesis, you must state this clearly rather than forcing a "perfect" conclusion.

2. Justifying with Uncertainties and Errors

A conclusion that is "fully consistent" requires the interpretation of processed data including its associated uncertainties.

• Final Values: Do not just state a single number. Express your resulting value alongside an experimentally acceptable range based on your measures of variation (e.g., standard deviation or range). Example format: (932 ± 15) J kg⁻¹ K⁻¹.
• Percentage Comparisons: Stating the percentage difference (compared to literature values) and percentage uncertainty is considered good practice.
• Error Impact: Comment on the presence of random and systematic errors shown in your graphs. You must state the direction of any systematic error and discuss how these errors affect your final conclusion.

3. The Scientific Context

To reach the highest mark band (5-6 marks), your conclusion must be justified through relevant comparison to accepted scientific context.

• Traceable Literature: Compare your findings with published values, general models, textbooks, or course notes. Ensure all sources are cited with enough detail to be traceable (e.g., include retrieval dates for online sources).
• Plausibility: If there is no accepted literature value for your specific system (e.g., the spring constant of a specific rubber band), you must determine and discuss whether your calculated result is physically plausible and reasonable.
• Assumptions: Identify the assumptions made when determining your equations or physics models. These can be practical (e.g., projectile motion remaining in a common plane), mathematical (e.g., assuming a parabolic path for a pendulum), or physical (e.g., treating air as an ideal gas).

Top-Band Checklist (5-6 Marks)

• Is my conclusion directly relevant to the research question?
• Is the conclusion fully consistent with my processed data and uncertainties?
• Did I express my final result with an acceptable uncertainty range?
• Did I discuss the impact and direction of systematic errors?
• Did I justify my conclusion by comparing it to traceable, accepted scientific context?

IB Physics IA Guidelines: The Evaluation Criterion

Overview of the Evaluation Criterion

The Evaluation criterion is worth a maximum of 6 marks, accounting for 25% of your total Internal Assessment (IA) grade. It assesses your ability to identify weaknesses and limitations in your methodology, evaluate their relative impact on your results, and suggest realistic improvements.

1. Identifying Weaknesses and Limitations

To score well, you must clearly understand the difference between weaknesses and limitations, and ensure they are specific to your methodology rather than generic statements.

• Weaknesses: These relate to the overall approach and procedural steps of your investigation. Examples include issues with the control of variables, the precision of measurement, or unexpected variation in the data. Do NOT include careless human errors (manipulation skills) or hypothetical events lacking evidence.
• Limitations: These refer to how your conclusion is restricted in scope. This could be due to the range of data collected, the physical confines of the system you are testing, or the applicability of the mathematical/physical assumptions made.

2. Evaluating the Relative Impact

It is not enough to simply list errors. For the top mark band (5-6 marks), you must explain the relative impact of specific methodological weaknesses or limitations on your final results.

• Qualitative Judgment: Assess whether the identified issues had a minor or major effect on the quality and reliability of your data.
• Error Analysis: Discuss how the precision of your instruments affected your data (random uncertainties). If your graphs reveal systematic shifts, state the direction of the systematic error and explicitly relate it back to a methodological weakness or faulty instrument.
• Database Investigations: If using a database, evaluate issues regarding the curation of the database itself or challenges with theoretical vs. experimental values, rather than questioning the general validity of the source.
• Evidence-Based: Your evaluation must be supported by the data analysis presented earlier in the report. Avoid unsupported speculation like "the temperature might have changed" unless you have evidence that it did and that it affected the results.

3. Suggesting Realistic Improvements

Your suggested improvements must directly address the specific weaknesses and limitations you have identified.

• Relevance: Do not list generic improvements. Only suggest changing to a more precise instrument if you specifically identified the current instrument's precision as a limitation.
• Feasibility: Improvements must be realistic and feasible to conduct within a standard school laboratory environment or field course.
• Avoid Generalities: Never use generic phrases like "take more measurements", "eliminate friction", or "perform in a vacuum" unless you provide specific, practical methods to achieve this that are directly tied to an identified flaw in your original procedure.

Top-Band Checklist (5-6 Marks)

• Did I clearly describe specific (not generic) methodological weaknesses or limitations?
• Did I explain the relative impact of these weaknesses on my final conclusion?
• Did I state the direction of systematic errors and tie them to procedural flaws?
• Are my suggested improvements realistic for a school setting?
• Do my improvements directly address the specific weaknesses I identified?