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This criterion assesses the extent to which the student has developed an appropriate and repeatable method to collect data that is relevant to the research question. The data could be primary or secondary, qualitative or quantitative. (Maximum 4 marks)
A descriptive, literature-based investigation that only reviews the literature cannot be considered a repeatable method.
| Do | Don't |
|---|---|
| Record the method actually used, including preliminary or trial investigations. | Submit a proposed plan or intended method. |
| Use standard protocols to determine the dependent variable and cite them. | Copy standard protocols verbatim (e.g., the Winkler method). |
| Ensure the methodology fits within a 10-hour period of investigative work. | Design a method that exceeds the available time without support or justification. |
| Include time for designing, trialling, and redesigning the method within the 10 hours. | Ignore the time needed for testing or refining your method. |
| Collect enough data to realistically answer the research question. | Collect an unrealistic or insufficient amount of data. |
| Provide a clear and detailed procedure that can be reproduced. | Write vague or overly general methods. |
| Use appropriate methodologies whether science-based or social science-based (e.g., field sampling, surveys, data mining). | Limit the study to only one kind of method when the research question allows for broader approaches. |
| Use replicate observations and multiple samples, especially in ecological studies. | Rely on single measurements or insufficient sampling. |
| Follow the 5 × 5 rule: 5 levels of the independent variable with 5 repeats each (e.g., 5 sites × 5 samples). | Skip replication—avoid studies with low data points unless justified. |
| Explain if time constraints prevent extensive data collection, especially in transect or field studies. | Omit justification for limited data in time-restricted studies. |
| Work in groups for fieldwork if needed, but ensure each student has an individual research question and sampling method. | Submit identical research questions or share identical data collection methods in group work. |
| Use class data in group or non-assessed work to increase replicates when necessary. | Rely solely on personal data collection if it limits replication. |
| Ensure enough survey participants (e.g., 30 per group) for statistically meaningful results. | Don’t conduct surveys with too few participants per category. |
The table below outlines the key dos and don’ts when designing a sampling strategy for ESS investigations to ensure that the data collected is representative, sufficient, and suitable for answering the research question. It is very important to justify how the data has been obtained.
| Do | Don't |
|---|---|
| Choose a sampling method that matches your research question and methodology | Pick a method just because it's common or convenient |
| Provide clear details on how samples and sample sites are selected | Be vague about where or how sampling happens |
| Use representative samples and aim for 30+ data points per group when needed | Collect too few or non-representative data points |
| Describe spacing and placement (e.g., quadrats every 2 m along transects) | Say “random spots” or skip spatial detail |
| Ensure the method is repeatable – include brand, type, or concentration if relevant | Generalize (e.g., just saying "factor 50" sunscreen) |
| Set a realistic sampling rate for digital tools – trial it first | Oversample (e.g., every second) unless justified |
| Explain how samples are collected and stored to maintain integrity | Ignore how storage can affect results |
In an ESS IA it is important to use illustrations and lists because they help present complex information clearly, making data, relationships, and strategies easier to understand and interpret for the reader.
| Do | Don't |
|---|---|
| Use annotated illustrations when they add value (e.g., maps, field site photos, diagrams) | Include pointless images (e.g., rulers, beakers, stopwatches) |
| Include site descriptions for fieldwork (with maps/photos if possible) | Skip context for field locations |
| Title and reference all illustrations clearly | Leave illustrations unlabeled or unreferenced |
| Use illustrations to clarify your investigation – they don’t count heavily toward the word limit | Worry about illustrations inflating word count |
| Provide a materials list if useful, or describe materials in the method section | Feel obligated to include a separate materials list |
Analysing uncertainties in the ESS IA is important because it demonstrates an understanding of the limitations and reliability of your data, shows awareness of potential errors or variability in measurements, and strengthens the validity of your conclusions.
| Type of Error | Cause | Example | How to Minimize |
|---|---|---|---|
| Random Variation | Natural differences in environment, materials, or organisms | Different growth rates in seedlings due to genetics or subtle condition differences | Use consistent sampling, control variables carefully, but complete elimination is not possible |
| Human Error | Mistakes due to lack of attention or care | Misreading instruments or skipping measurements | Plan carefully, stay focused, and use data loggers for repeated measurements |
| Systematic Error | Faulty or uncalibrated equipment, procedural flaws | Incorrect readings from an uncalibrated thermometer | Regularly check and calibrate equipment, use controls (e.g., blank for colorimeter) |
The aim is to show critical thinking and reliability awareness, rather than detailed statistical error analysis expected in more data-heavy sciences.
If an examiner identifies an experiment that poses serious safety or environmental risks, the IB may be notified, and the school could be contacted for further action.
Risk assessment: Although no longer directly assessed, students are still expected to conduct investigations that are safe, ethical, and environmentally responsible.